[ascl:2201.014]
nProFit: n-Profile Fitting tool

nProFit analyzes surface brightness profiles. It obtains the best-fit structural, scale, and shape parameters of star clusters in Hubble Space Telescope images of nearby galaxies. The code fits dynamical models and can derive physically-relevant parameters. Among these are central volume and luminosity densities, total masses and luminosities, central velocity dispersions, core radius, half-light radius, tidal radius, and binding energy.

[ascl:2202.001]
GA Galaxy: Interacting galaxies model fitter

GA Galaxy fits models of interacting galaxies to synthetic data using a genetic algorithm and custom fitness function. The genetic algorithm is real-coded and uses a mixed Gaussian kernel for mutation. The fitness function incorporates 1.) a direct pixel-to-pixel comparison between the target and model images and 2.) a comparison of the degree of tidal distortion present in the target and model image such that target-model pairs which are similarly distorted will have a higher relative fitness. The genetic algorithm is written in Python 2.7 while the simulation code (SPAM: Stellar Particle Animation Module) is written in Fortran 90.

[submitted]
frbmclust: Model-independent classification of events from the first CHIME/FRB Fast Radio Burst catalog

CHIME/FRB instrument has recently published a catalog containing about half of thousand fast radio bursts (FRB) including their spectra and several reconstructed properties, like signal widths, amplitudes, etc. We have developed a model-independent approach for the classification of these bursts using cross-correlation and clustering algorithms applied to one-dimensional intensity profiles, i.e. to amplitudes as a function of time averaged over the frequency. This approach is implemented in frbmclust package, which is used for classification of bursts featuring different waveform morphology.

[ascl:2202.002]
NWelch: Spectral analysis of time series with nonuniform observing cadence

NWelch uses Welch's method to estimate the power spectra, complex cross-spectrum, magnitude-squared coherence, and phase spectrum of bivariate time series with nonuniform observing cadence. For univariate time series, users can apply the Welch's power spectrum estimator or compute a nonuniform fast Fourier transform-based periodogram. Options include tapering in the time domain and computing bootstrap false alarm levels. Users may choose standard 50%-overlapping Welch's segments or apply a custom-made segmentation scheme. NWelch was designed for Doppler planet searches but may be applied to any type of time series.

[ascl:2202.003]
Zwindstroom: Cosmological growth factors from fluid calculations

Zwindstroom computes background quantities and scale-dependent growth factors for cosmological models with free-streaming species, such as massive neutrinos. Following the earlier REPS code (ascl:1612.022), the code uses a Newtonian fluid approximation with external neutrino sound speed to close the Boltzmann hierarchy. Zwindstroom supports multi-fluid models with distinct transfer functions and sound speeds. A flexible python interface facilitates interaction with CLASS (ascl:1106.020) through classy. There is also a Zwindstroom plugin for the cosmological initial conditions generator monofonIC (ascl:2008.024) that allows for higher-order LPT ICs for massive neutrino simulations in a single step.

[ascl:2202.004]
SUPPNet: Spectrum normalization neural network

SUPPNet performs fully automated precise continuum normalization of merged echelle spectra and offers flexible manual fine-tuning, if necessary. The code uses a fully convolutional deep neural network (SUPP Network) trained to predict a pseudo-continuum. The post-processing step uses smoothing splines that give access to regressed knots, which are useful for optional manual corrections. The active learning technique controls possible biases that may arise from training with synthetic spectra and extends the applicability of the method to features absent in this kind of spectra.

[ascl:2202.005]
palettable: Color palettes for Python

Palettable is a library of color palettes for Python. The code is written in pure Python with no dependencies; it can be used to supply color maps for matplotlib plots, customize matplotlib plots, and to supply colors for a web application.

[ascl:2202.006]
FIRE Studio: Movie making utilities for the FIRE simulations

FIRE Studio is a Python interface for C libraries that project Smoothed Particle Hydrodynamic (SPH) datasets. These C libraries can, in principle, be applied to any SPH dataset; the Python interface is specialized to conveniently load and format Gadget-derivative datasets such as GIZMO (ascl:1410.003). FIRE Studio is fast, memory efficient, and parallelizable. In addition to producing "1-color" projection maps for SPH datasets, the interface can produce "2-color" maps, where the pixel saturation is set by one projected quantity and the hue is set by another, and "3-color" maps, where three quantities are projected simultaneously and remapped into an RGB colorspace. FIRE Studio can model stellar emission and dust extinction to produce mock Hubble images (by default) or to model surface brightness maps for thirteen of the most common bands (plus the bolometric luminosity). It produces publication quality static images of simulation datasets and provides interpolation scripts to create movies that smoothly evolve in time (provided multiple snapshots in time of the data exist), view the dataset from different perspectives (taking advantage of shared memory buffers to allow massive parallelization), or both.

[ascl:2202.007]
SciCatalog: Tools for scientific data catalogs

SciCatalog handles catalogs of scientific data in a way that is easily extensible, including the ability to create nicely formatted AASTex deluxe tables for use in AAS Publishing manuscripts. It handles catalogs of values, their positive and negative uncertainties, and references for those values with methods for easily adding columns and changing values. The catalog is also backed up every time it is loaded under the assumption that it is about to be modified.

[ascl:2202.008]
TERRA: Transit detection code

TERRA (Transiting Exoearth Robust Reduction Algorithm) identifies and removes instrumental noise in Kepler photometry. This transit detection code is optimized to detect small planets around photometrically quiet stars. TERRA calculates photometry in the time domain, combs the calibrated photometry for periodic, box-shaped signals, fits promising signals, and rejects signals inconsistent with exoplanet transits.

[ascl:2202.009]
EDIV: Exoplanet Detection Identifier Vetter

Zink, Jon K.; Hardegree-Ullman, Kevin K.; Christiansen, Jessie L.,; Dressing, Courtney D.; Crossfield, Ian J.M.; Petigura, Erik A.; Schlieder, Joshua E.; Ciardi, David R.

EDI (Exoplanet Detection Identifier) Vetter identifies false positive transit signal in the K2 data set. It combines the functionalities of Terra (ascl:2202.008) and RoboVetter (ascl:2012.006) and is optimized to test single transiting planet signals. An easily implemented suite of vetting metrics built to run alongside TLS of EDI Vetter, EDI-Vetter Unplugged (ascl:2202.010), is also available.

[ascl:2202.010]
EDIVU: Exoplanet Detection Identifier Vetter Unplugged

Zink, Jon K.; Hardegree-Ullman, Kevin K.; Christiansen, Jessie L.,; Dressing, Courtney D.; Crossfield, Ian J.M.; Petigura, Erik A.; Schlieder, Joshua E.; Ciardi, David R.

The EDI (Exoplanet Detection Identifier) Vetter Unplugged software identifies false positive transit signals using Transit Least Squares (TLS) information and has been simplified from the full EDI-Vetter algorithm (ascl:2202.009) for easy implementation with the TLS output.

[ascl:2202.011]
RFEP: Residual Feature Extraction Pipeline

Residual Feature Extraction Pipeline carries out feature extraction of residual substructure within the residual images produced by popular galaxy structural-fitting routines such as GALFIT (ascl:1104.010) and GIM2D (ascl:1004.001). It extracts faint low surface brightness features by isolating flux-wise and area-wise significant contiguous pixels regions by rigorous masking routine. The code accepts the image cubes (original image, model image, residual image) and generates several data products, such as an image with extracted features, a source extraction based segmentation map, and the background sky mask and the residual extraction mask. It uses a Monte Carlo approach-based area threshold above which the extracted features are identified. The pipeline also creates a catalog entry indicating the surface brightness and its error.

[ascl:2202.012]
fiducial_flare: Spectra and lightcurves of a standardized far ultraviolet flare

fiducial_flare generates a reasonable approximation of the UV emission of M dwarf stars over a single flare or a series of them. The simulated radiation is resolved in both wavelength and time. The intent is to provide consistent input for applications requiring time-dependent stellar UV radiation fields that balances simplicity with realism, namely for simulations of exoplanet atmospheres.

[ascl:2202.013]
PSLS: PLATO Solar-like Light-curve Simulator

Samadi, R.; Deru, A.; Reese, D.; Marchiori, V.; Grolleau, E.; Green, J. J.; Pertenais, M.; Lebreton, Y.; Deheuvels, S.; Mosser, B.; Belkacem, K.; Börner, A.; Smith, A. M. S.

PSLS simulates solar-like oscillators representative of PLATO targets. It includes planetary transits, stochastically-excited oscillations, granulation and activity background components, as well as instrumental systematic errors and random noises representative for PLATO.

[ascl:2202.014]
Citlalicue: Create and manipulate stellar light curves

Citlalicue allows you to create synthetic stellar light curves (transits, stellar variability and white noise) and detrend light curves using Gaussian Processes (GPs). Transits are implemented using PyTransit (ascl:1505.024). Python notebooks are provided to demonstrate using Citlalicue for both functions.

[submitted]
Mean Motion Resonances

Site with collection of codes and fundamental references on mean motion resonances.

[ascl:2202.015]
SPARTAN: SPectroscopic And photometRic fiTting tool for Astronomical aNalysis

SPARTAN fits the spectroscopy and photometry of distant galaxies. The code implements multiple interfaces to help in the configuration of the fitting and the inspection of the results. SPARTAN relies on pre-computed input files (such as stellar population and IGM extinction), available for download, to save time in the fitting process.

[ascl:2202.016]
Find_Orb: Orbit determination from observations

Find_Orb takes a set of observations of an asteroid, comet, or natural or artificial satellite given in the MPC (Minor Planet Center) format, the ADES astrometric format, and/or the NEODyS or AstDyS formats, and finds the corresponding orbit.

[ascl:2202.017]
GALLUMI: GALaxy LUMInosity function pipeline

GALLUMI (GALaxy LUMInosity) is a likelihood code that extracts cosmological and astrophysical parameters from the UV galaxy luminosity function. The code is implemented in the MCMC sampler MontePython (ascl:1307.002) and can be readily run in conjunction with other likelihood codes.

[ascl:2202.018]
Sculptor: Interactive modeling of astronomical spectra

Sculptor manipulates, models and analyzes spectroscopic data; the code facilitates reproducible scientific results and easy to inspect model fits. A built-in graphical user interface around LMFIT (ascl:1606.014) offers interactive control to set up and combine multiple spectral models to fully fit the spectrum of choice. Alternatively, all core functionality can be scripted to facilitate the design of spectral fitting and analysis pipelines.

[ascl:2202.019]
Contaminante: Identify blended targets in Kepler, TESS, and K2 data

contaminante helps find the contaminant transiting source in NASA's Kepler, K2 or TESS data. When hunting for transiting planets, sometimes signals come from neighboring contaminants. This package helps users identify where the transiting signal comes from in their data. The code uses pixel level modeling of the TargetPixelFile data from NASA's astrophysics missions that are processed with the Kepler pipeline. The output of contaminante is a Python dictionary containing the source location and transit depth, and a contaminant location and depth. It can also output a figure showing where the main target is centered in all available TPFs, what the phase curve looks like for the main target, where the transiting source is centered in all available TPFs, if a transiting source is located outside the main target, or the transiting source phase curve, if a transiting source is located outside the main target.

[ascl:2202.020]
distance-omnibus: Distance estimation method for molecular cloud clumps in the Milky Way

distance-omnibus computes posterior DPDFs for catalog sources using the Bayesian application of kinematic distance likelihoods derived from a Galactic rotation curve with prior Distance Probability Density Functions (DPDFs) derived from ancillary data. The methodology and code base are generalized for use with any (sub-)millimeter survey of the Galactic plane.

[ascl:2202.021]
popsynth: Observed surveys from latent population models

Popsynth provides an abstract way to generate survey populations from arbitrary luminosity functions and redshift distributions. Additionally, auxiliary quantities can be sampled and stored. Populations can be saved and restored via an HDF5 files for later use, and population synthesis routines can be created via classes or structured YAML files. Users can construct their own classes for spatial, luminosity, and other distributions, all of which can be connected to arbitrarily complex selection functions.

[ascl:2202.022]
ASPIRED: Automated SpectroPhotometric Image REDuction

ASPIRED reduces 2D spectral data from raw image to wavelength and flux calibrated 1D spectrum automatically without any user input (quicklook quality), and provides a set of easily configurable routines to build pipelines for long slit spectrographs on different telescopes (science quality). It delivers near real-time data reduction, which can facilitate automated or interactive decision making, allowing "on-the-fly" modification of observing strategies and rapid triggering of other facilities.

[ascl:2202.023]
Starduster: Radiative transfer and deep learning multi-wavelength SED model

The deep learning model Starduster emulates dust radiative transfer simulations, which significantly accelerates the computation of dust attenuation and emission. Starduster contains two specific generative models, which explicitly take into account the features of the dust attenuation curves and dust emission spectra. Both generative models should be trained by a set of characteristic outputs of a radiative transfer simulation. The obtained neural networks can produce realistic galaxy spectral energy distributions that satisfy the energy balance condition of dust attenuation and emission. Applications of Starduster include SED-fitting and SED-modeling from semi-analytic models.

[ascl:2202.024]
SunnyNet: Neural network framework for solving 3D NLTE radiative transfer in stellar atmospheres

SunnyNet learns the mapping the between LTE and NLTE populations of a model atom and predicts the NLTE populations based on LTE populations for an arbitrary 3D atmosphere. To use SunnyNet, one must already have a set of LTE and NLTE populations computed in 3D, to train the network. These must come from another code, as SunnyNet is unable to solve the formal problem. Once SunnyNet is trained, one can feed it LTE populations from a different 3D atmosphere, and obtain predicted NLTE populations. The NLTE populations can then be used to synthesize any spectral line that is included in the model atom. SunnyNet's output is a file with predicted NLTE populations. SunnyNet itself does not calculate synthetic spectra, but a sample script written in the Julia language that quickly computes Hα spectra is included.

[ascl:2202.025]
INSANE: INflationary potential Simulator and ANalysis Engine

INSANE (INflationary potential Simulator and ANalysis Engine) takes either a numeric inflationary potential or a symbolic one, calculates the background evolution and then, using the Mukhanov-Sasaki equations, calculates the primordial power spectrum it yields. The package can analyze the results to extract the spectral index n_s, the index running alpha, the running of running and possibly higher moments. The package contains two main modules: BackgroundSolver solves the background equations, and the MsSolver module solves and analyses the MS equations.

[ascl:2202.026]
topoaccel: Topological acceleration scripts

topoaccel calculates topological acceleration for several of the S^3 quotient spaces considered 'regular', in that they have a Platonic solid as one of their fundamental domain shapes, and are globally homogeneous. The topoaccel scripts can be run using the free-licensed software package Maxima (https://maxima.sourceforge.io/documentation.html).

[submitted]
RCETC: Roman Coronagraph Exposure Time Calculator

Hildebrandt, S.R.; Spohn, C.; Nemati, B.; Savransky, D.; Bailey, V.; Debes, J.; Douglas, E.S.; Keithly, D.; Kern, B.; Sheldon, L.J.

The Roman Coronagraph Exposure Time Calculator (Roman_Coronagraph_ETC for short) is the public version of the exposure time calculator of the Coronagraph Instrument aboard the Nancy Grace Roman Space Telescope funded by NASA. The methods used to estimate the integration times are based upon peer reviewed research articles (see Bibliography) and a collection of instrumental and modeling parameters of both the Coronagraph Instrument and the Nancy Grace Roman Space Telescope. The code is written in python. Visit https://github.com/hsergi/Roman_Coronagraph_ETC for more information.

[ascl:2203.001]
SISTER: Starshade Imaging Simulation Toolkit for Exoplanet Reconnaissance

SISTER (Starshade Imaging Simulations Toolkit for Exoplanet Reconnaissance) predicts how an exoplanet system would look in an instrument that utilizes an Starshade to block the light from the host star. The tool allows for controlling a set of parameters of the whole instrument for: (1) the Starshade design, (2) the exoplanetary system, (3) the telescope and (4) the camera. SISTER includes plotting software, and can also store simulations on disk for plotting with other software.

[ascl:2203.002]
exoVista: Planetary systems generator

exoVista generates a "universe" of planetary systems, creating thousands of models of quasi-self-consistent planetary systems around known nearby stars at scattered light wavelengths. It efficiently records the position, velocity, spectrum, and physical parameters of all bodies as functions of time. exoVista models can be used for simulating surveys using the direct imaging, transit, astrometric, and radial velocity techniques.

[ascl:2203.003]
NIMBLE: Non-parametrIc jeans Modeling with B-spLinEs

NIMBLE (Non-parametrIc jeans Modeling with B-spLinEs) inferrs the cumulative mass distribution of a gravitating system from full 6D phase space coordinates of its tracers via spherical Jeans modeling. It models the Milky Way's dark matter halo using Gaia and Dark Energy Spectroscopic Instrument Milky Way Survey (DESI MWS) data. NIMBLE includes a basic inverse modeling Jeans routine that assumes perfect and complete data is available and a more complex forward modeling Jeans routine that deconvolves observational effects (uncertainties and limited survey volume) characteristic of Gaia and the DESI-MWS. It also includes tools for generating simple equilibrium model galaxies using Agama (ascl:1805.008) and imposing mock Gaia+DESI errors on 6D phase space input data.

[ascl:2203.004]
imexam: IMage EXAMination and plotting

Sosey, Megan; Bradley, Larry; Sipőcz, Brigitta; Yoachim, Peter; Jeschke, Eric; Lim, P. L.; Tollerud, Erik; Craig, Matt; Bray, E. M.; Kurtz, Heather; Soref, Josh; Robitaille, Thomas; Hoyt, Taylor; Deil, Christoph; Eisenhamer, Jonathan

imexam performs simple image examination and plotting, with similar functionality to IRAF's (ascl:9911.002) imexamine. It is a lightweight library that enables users to explore data from a command line interface, through a Jupyter notebook, or through a Jupyter console. imexam can be used with multiple viewers, such as DS9 (scl:0003.002) or Ginga (ascl:1303.020), or without a viewer as a simple library to make plots and grab quick photometry information. It has been designed so that other viewers may be easily attached in the future.

[ascl:2203.005]
pygacs: Toolkit to manipulate Gaia catalog tables

pygacs manipulates Gaia catalog tables hosted at ESA's Gaia Archive Core Systems (GACS). It provides python modules for the access and manipulation of tables in GACS, such as a basic query on a single table or crossmatch between two tables. It employs the TAP command line access tools described in the Help section of the GACS web pages. Both public and authenticated access have been implemented.

[ascl:2203.006]
starry_process: Interpretable Gaussian processes for stellar light curves

starry_process implements an interpretable Gaussian process (GP) for modeling stellar light curves. The code's hyperparameters are physically interpretable, and include the radius of the spots, the mean and variance of the latitude distribution, the spot contrast, and the number of spots, among others. The rotational period of the star, the limb darkening parameters, and the inclination (or marginalize over the inclination if it is not known) can also be specified.

[ascl:2203.007]
GAMERA: Source modeling in gamma astronomy

GAMERA handles spectral modeling of non-thermally emitting astrophysical sources in a simple and modular way. It allows the user to devise time-dependent models of leptonic and hadronic particle populations in a general astrophysical context (including SNRs, PWNs and AGNs) and to compute their subsequent photon emission. GAMERA can calculate the spectral evolution of a particle population in the presence of time-dependent or constant injection, energy losses and particle escape; it also calculates the radiation spectrum from a parent particle population.

[ascl:2203.008]
MIRaGe: Multi Instrument Ramp Generator

Hilbert, Bryan; Sahlmann, Johannes; Volk, Kevin; Osborne, Shannon; dthatte; Perrin, Marshall; Chambers, Lauren; Slavich, Edward; Taylor, Jo; Tollerud, Erik; Lim, P. L.

MIRaGe creates simulated exposures for NIRCam’s imaging and wide field slitless spectroscopy (WFSS) modes, NIRISS’s imaging, WFSS, and aperture masking interferometery (AMI) modes, and FGS’s imaging mode. It supports sidereal as well as non-sidereal tracking; for example, sources can be made to move across the field of view within an observation.

[ascl:2203.009]
fleck: Fast starspot rotational modulation light curves

fleck simulates rotational modulation of stars due to starspots and is used to overcome the degeneracies and determine starspot coverages accurately for a sample of young stars. The code simulates starspots as circular dark regions on the surfaces of rotating stars, accounting for foreshortening towards the limb, and limb darkening. Supplied with the latitudes, longitudes, and radii of spots and the stellar inclinations from which each star is viewed, fleck takes advantage of efficient array broadcasting with numpy to return approximate light curves. For example, the code can compute rotational modulation curves sampled at ten points throughout the rotation of each star for one million stars, with two unique spots each, all viewed at unique inclinations, in about 10 seconds on a 2.5 GHz Intel Core i7 processor. This rapid computation of light curves en masse makes it possible to measure starspot distributions with techniques such as Approximate Bayesian Computation.

[ascl:2203.010]
D2O: Distributed Data Object

D2O acts as a layer of abstraction between algorithm code and data-distribution logic to manage cluster-distributed multi-dimensional numerical arrays; this provides usability without losing numerical performance and scalability. D2O's global interface makes the cluster node's local data directly accessible for use in customized high-performance modules. D2O is written in Python; the code is portable and easy to use and modify. Expensive operations are carried out by dedicated external libraries like numpy and mpi4py and performance scales well when moving to an MPI cluster. In combination with NIFTy, D2O enables supercomputer based astronomical imaging via RESOLVE (ascl:1505.028) and D3PO (ascl:1504.018).

[ascl:2203.011]
SATCHEL: Pipeline to search for long-period exoplanet signals

SATCHEL (Search Algorithm for Transits in the Citizen science Hunt for Exoplanets in Lightcurves) searches for individual signals of interest in time-series data classified through crowdsourcing. The pipeline was built for the purpose of finding long-period exoplanet transit signals in Kepler photometric time-series data, but may be adapted for searches for any kind of one-dimensional signals in crowdsourced classifications.

[ascl:2203.012]
pyobs: Python framework for autonomous astronomical observatories

pyobs enables remote and fully autonomous observation control of astronomical telescopes. It provides an abstraction layer over existing drivers and a means of communication between different devices (called modules in pyobs). The code can also act as a hardware driver for all the devices used at an observatory. In addition, pyobs offers non-hardware-related modules for automating focusing, acquisition, guiding, and other routine tasks.

[ascl:2203.013]
PetroFit: Petrosian properties calculator and galaxy light profiles fitter

Geda, Robel; Crawford, Steven; Hunt, Lucas R.; Bershady, Matthew A.; Tollerud, Erik J.; Randriamampandry, Solohery M.

PetroFit calculates Petrosian properties, such as radii and concentration indices; it also fits galaxy light profiles. The package, built on Photutils (ascl:1609.011), includes tools for performing accurate photometry, segmentations, Petrosian properties, and fitting.

[ascl:2203.014]
AutoSourceID-Light: Source localization in optical images

Stoppa, Fiorenzo; Vreeswijk, Paul; Bloemen, Steven; Bhattacharyya, Saptashwa; Caron, Sascha; Jóhannesson, Guðlaugur; Ruiz de Austri, Roberto; van den Oetelaar, Chris; Zaharijas, Gabrijela; Groot, Paul. J.; Cator, Eric; Nelemans, Gijs

AutoSourceID-Light (ASID-L) analyzes optical imaging data using computer vision techniques that can naturally deal with large amounts of data. The framework rapidly and reliably localizes sources in optical images.

[ascl:2203.015]
easyFermi: Fermi-LAT data analyzer

easyFermi provides a user-friendly graphical interface for basic to intermediate analysis of Fermi-LAT data in the framework of Fermipy (ascl:1812.006). The code can measure the gamma-ray flux and photon index, build spectral energy distributions, light curves, test statistic maps, test for extended emission, and relocalize the coordinates of gamma-ray sources. Tutorials for easyFermi are available on YouTube and GitHub.

[ascl:2203.016]
MaNGA-DRP: MaNGA Data Reduction Pipeline

Law, David R.; Cherinka, Brian; Yan, Renbin; Andrews, Brett H.; Bershady, Matthew A.; Bizyaev, Dmitry; Blanc, Guillermo A.; Blanton, Michael R.; Bolton, Adam S.; Brownstein, Joel R.; Bundy, Kevin; Chen, Yanmei; Drory, Niv; D'Souza, Richard; Fu, Hai; Jones, Amy; Kauffmann, Guinevere; MacDonald, Nicholas; Masters, Karen L.; Newman, Jeffrey A.; Parejko, John K.; Sánchez-Gallego, José R.; Sánchez, Sebastian F.; Schlegel, David J.; Thomas, Daniel; Wake, David A.; Weijmans, Anne-Marie; Westfall, Kyle B.; Zhang, Kai

The MaNGA Data Reduction Pipeline (DRP) processes the raw data to produce flux calibrated, sky subtracted, coadded data cubes from each of the individual exposures for a given galaxy. The DRP consists of two primary parts: the 2d stage that produces flux calibrated fiber spectra from raw individual exposures, and the 3d stage that combines multiple flux calibrated exposures with astrometric information to produce stacked data cubes. These science-grade data cubes are then processed by the MaNGA Data Analysis Pipeline (ascl:2203.017), which measures the shape and location of various spectral features, fits stellar population models, and performs a variety of other analyses necessary to derive astrophysically meaningful quantities from the calibrated data cubes.

[ascl:2203.017]
MaNGA-DAP: MaNGA Data Analysis Pipeline

Westfall, Kyle B.; Cappellari, Michele; Bershady, Matthew A.; Bundy, Kevin; Belfiore, Francesco; Ji, Xihan; Law, David R.; Schaefer, Adam; Shetty, Shravan; Tremonti, Christy A.; Yan, Renbin; Andrews, Brett H.; Brownstein, Joel R.; Cherinka, Brian; Coccato, Lodovico; Drory, Niv; Maraston, Claudia; Parikh, Taniya; Sánchez-Gallego, José R.; Thomas, Daniel; Weijmans, Anne-Marie; Barrera-Ballesteros, Jorge; Du, Cheng; Goddard, Daniel; Li, Niu; Masters, Karen; Ibarra Medel, Héctor Javier; Sánchez, Sebastián F.; Yang, Meng; Zheng, Zheng; Zhou, Shuang

The MaNGA data analysis pipeline (MaNGA DAP) analyzes the data produced by the MaNGA data-reduction pipeline (ascl:2203.016) to produced physical properties derived from the MaNGA spectroscopy. All survey-provided properties are currently derived from the log-linear binned datacubes (*i.e.*, the LOGCUBE files).

[ascl:2203.018]
sympy2c: Generating fast C/C++ functions and ODE solvers from symbolic expressions

The Python package sympy2c allows creation and compilation of fast C/C++ based extension modules from symbolic representations. It can create fast code for the solution of high dimensional ODEs, or numerical evaluation of integrals where sympy fails to compute an anti-derivative. Based on the symbolic formulation of a stiff ODE, sympy2c analyzes sparsity patterns in the Jacobian matrix of the ODE, and generates loop-less fast code by unrolling loops in the internally used LU factorization algorithm and by avoiding unnecessary computations involving known zeros.

[ascl:2203.019]
agnpy: Modeling jetted Active Galactic Nuclei radiative processes with Python

agnpy focuses on the numerical computation of the photon spectra produced by leptonic radiative processes in jetted Active Galactic Nuclei (AGN). It includes classes describing the galaxy components responsible for line and thermal emission and calculates the absorption due to gamma-gamma pair production on soft (IR-UV) photon fields.

[ascl:2203.020]
MAMPOSSt: Mass/orbit modeling of spherical systems

MAMPOSSt (Modeling Anisotropy and Mass Profiles of Observed Spherical Systems) is a Bayesian code to perform mass/orbit modeling of spherical systems. It determines marginal parameter distributions and parameter covariances of parametrized radial distributions of dark or total matter, as well as the mass of a possible central black hole, and the radial profiles of density and velocity anisotropy of one or several tracer components, all of which are jointly fit to the discrete data in projected phase space. It is based upon the MAMPOSSt likelihood function for the distribution of individual tracers in projected phase space (projected radius and line-of-sight velocity) and the CosmoMC Markov Chain Monte Carlo code (ascl:1106.025), run in generic mode. MAMPOSSt is not based on the 6D distribution function (which would require triple integrals), but on the assumption that the local 3D velocity distribution is an (anisotropic) Gaussian (requiring only a single integral).

[ascl:2203.021]
MG-MAMPOSSt: Test gravity with the mass profiles of galaxy clusters

MG-MAMPOSSt extends the MAMPOSSt code (ascl:2203.020), which performs Bayesian fits of models of mass and velocity anisotropy profiles to the distribution of tracers in projected phase space, to handle modified gravity models and constrain its parameters. It implements two distinct types of gravity modifications: general chameleon (including $f(\mathcal{R})$ models), and beyond Horndeski gravity (Vainshtein screening). MG-MAMPOSSt efficently explores the parameter space either by computing the likelihood over a multi-dimensional grid of points or by performing a simple Metropolis-Hastings MCMC. The code requires a Fortran90 compiler or higher and makes use of the getdist package (ascl:1910.018) to plot the marginalized distributions in the MCMC mode.

[ascl:2203.022]
Vetting: Stand-alone tools for vetting transit signals in Kepler, K2 and TESS data

vetting contains simple, stand-alone Python tools for vetting transiting signals in NASA's Kepler, K2, and TESS data. The code performs a centroid test to look for significant changes in the centroid of a star during a transit or eclipse. vetting requires an installation of Python 3.8 or higher.

[ascl:2203.023]
MAGRATHEA: Multi-processor Adaptive Grid Refinement Analysis for THEoretical Astrophysics

MAGRATHEA (Multi-processor Adaptive Grid Refinement Analysis for THEoretical Astrophysics) is a foundational cosmological library and a relativistic raytracing code. Classical linear algebra libraries come with their own operations and can be difficult to leverage for new data types. Instead of providing basic types, MAGRATHEA provides tools to generate base types such as scalar quantities, points, vectors, or tensors.

[ascl:2203.024]
Magrathea-Pathfinder: 3D AMR ray-tracing in simulations

Magrathea-Pathfinder propagates photons within cosmological simulations to construct observables. This high-performance framework uses a 3D Adaptive-Mesh Refinement and is built on top of the MAGRATHEA metalibrary (ascl:2203.023).

[ascl:2203.025]
SetCoverPy: A heuristic solver for the set cover problem

SetCoverPy finds an (near-)optimal solution to the set cover problem (SCP) as fast as possible. It employs an iterative heuristic approximation method, combining the greedy and Lagrangian relaxation algorithms. It also includes a few useful tools for a quick chi-squared fitting given two vectors with measurement errors.

[ascl:2203.026]
axionCAMB: Modification of the CAMB Boltzmann code

axionCAMB is a modified version of the publicly available code CAMB (ascl:1102.026). axionCAMB computes cosmological observables for comparison with data. This is normally the CMB power spectra (T,E,B,\phi in auto and cross power), but also includes the matter power spectrum.

[ascl:2203.027]
Zoobot: Deep learning galaxy morphology classifier

Walmsley, Mike; Lintott, Chris; Géron, Tobias; Kruk, Sandor; Krawczyk, Coleman; Willett, Kyle W.; Bamford, Steven; Kelvin, Lee S.; Fortson, Lucy; Gal, Yarin; Keel, William; Masters, Karen L.; Mehta, Vihang; Simmons, Brooke D.; Smethurst, Rebecca; Smith, Lewis; Baeten, Elisabeth M.; Macmillan, Christine

Zoobot classifies galaxy morphology with Bayesian CNN. Deep learning models were trained on volunteer classifications; these models were able to both learn from uncertain volunteer responses and predict full posteriors (rather than point estimates) for what volunteers would have said. The code reproduces and improves Galaxy Zoo DECaLS automated classifications, and can be finetuned for new tasks.

[ascl:2203.028]
SimLine: Radiative transfer in molecular lines

SimLine computes the profiles of molecular rotational transitions and atomic fine structure lines in spherically symmetric clouds with arbitrary density, temperature and velocity structure. The code is designed towards a maximum flexibility and very high accuracy based on a completely adaptive discretization of all quantities. The code can treat arbitrary species in spherically symmetric configurations with arbitrary velocity structures and optical depths between about -5 and 5000. Moreover, SimLine includes the treatment of turbulence and clumping effects in a local statistical approximation combined with a radial dependence of the correlation parameters. The code consists of two parts: the self-consistent solution of the balance equations for all level populations and energy densities at all radial points and the computation of the emergent line profiles observed from a telescope with finite beam width and arbitrary offset.

[ascl:2203.029]
Bootsik: Potential field calculator

The Bootsik software generates and visualizes potential magnetic fields. bootsik.f90 generates a potential magnetic field on a 3D mesh, staggered relative to the magnetic potential, by extrapolating the magnetic field normal to the photospheric surface. The code first calculates a magnetic potential using a modified Green’s function method and then uses a finite differencing scheme to calculate the magnetic field from the potential. The IDL script boobox.pro can then be used to visualize the magnetic field.

[ascl:2203.030]
Wigglewave: Linearized governing equations solver

Wigglewave uses a finite difference method to solve the linearized governing equations for a torsion Alfvèn wave propagating in a plasma with negligible plasma beta and in a force-free axisymmetric magnetic field with no azimuthal component embedded in a high density divergent tube structure. Wigglewave is fourth order in time and space using a fourth-order central difference scheme for calculating spatial derivatives and a fourth-order Runge-Kutta (RK4) scheme for updating at each timestep. The solutions calculated are the perturbations to the velocity, v and to the magnetic field, b. All variables are calculated over a uniform grid in radius r and height z.

[ascl:2203.031]
TAWAS: Wave equation solver

TAWAS solves the wave equation for torsional Alfvèn waves in a viscous plasma. The background magnetic field is axisymmetric and force-free with no azimuthal component and the plasma beta is assumed to be negligible. The solution is calculated over a uniform numerical grid with coordinates r and z for the radius and height respectively. TAWAS, written in IDL, requires no input files. The problem parameters at the top of the code can be changed as need. The 'plotting' variable determines which plots are shown by the script; the code contains several options for plotting. Outputs can be saved to a specific location by changing the variables save_dir and run_name listed just below the parameters. The code outputs include solutions for the velocity perturbation, the magnetic field perturbation and the wave energy flux.

[ascl:2204.001]
TG: Turbulence Generator

Turbulence Generator generates a time sequence of random Fourier modes via an Ornstein-Uhlenbeck (OU) process, used to drive turbulence in hydrodynamical simulation codes. It can also generate single turbulent realizations. Turbulence driving based on this method is currently supported by implementations in AREPO (ascl:1909.010), FLASH (ascl:1010.082), GADGET (ascl:0003.001), PHANTOM (ascl:1709.002), PLUTO (ascl:1010.045), and Quokka (ascl:2110.009).

[ascl:2204.002]
Astroplotlib: Python scripts to handle astronomical images

Astroplotlib builds images with any scale, overlay contours, physical bars, and orientation arrows (N and E axes) automatically. The package contains scripts to overlay pseudo-slits and obtain statistics from apertures, estimate the background sky, and overlay the fitted isophotes and their respective contours on an image. Astroplotlib can work with the output table from the Ellipse task of IRAF and overlay fitted isophotes and their respective contours. It includes a GUI for masking areas in the images by using different polygons, and can also obtain statistical information (*e.g.*, total flux and mean, among others) from the masked areas. There is also a GUI to overlay star catalogs on an image and an option to download them directly from the Vizier server.

[ascl:2204.003]
legacystamps: Retrieve DESI Legacy Imaging Surveys cutouts

The Python module legacystamps provides easy retrieval, both standalone and scripted, of FITS and JPEG cutouts from the DESI Legacy Imaging Surveys through URLs provided by the Legacy Survey viewer.

[ascl:2204.004]
Bayesian SZNet: Bayesian deep learning to predict redshift with uncertainty

Bayesian SZNet predicts spectroscopic redshift through use of a Bayesian convolutional network. It uses Monte Carlo dropout to associate predictions with predictive uncertainties, allowing the user to determine unusual or problematic spectra for visual inspection and thresholding to balance between the number of incorrect redshift predictions and coverage.

[ascl:2204.005]
TESS-Localize: Localize variable star signatures in TESS Photometry

TESS-Localize identifies the location on the target pixel files (TPF) where sources of variability found in the aperture originate. The user needs only to provide a list of frequencies found in the aperture that belong to the same source and the number of principal components needed to be removed from the light curve to ensure it is free of systematic trends.

[ascl:2204.006]
dsigma: Galaxy-galaxy lensing Python package

dsigma analyzes galaxy-galaxy lensing. Written in Python, it has a broadly applicable API and is optimized for computational efficiency. While originally intended to be used with the shape catalog of the Hyper-Suprime Cam (HSC) survey, it should work for other surveys, most prominently the Dark Energy Survey (DES) and the Kilo-Degree Survey (KiDS).

[ascl:2204.007]
RTS: Radio Transient Simulations

Radio Transient Simulations uses Monte-Carlo simulations to accurately determine transient rates in radio surveys. The user inputs either a real or simulated observational setup, and the simulations code calculates transient rate as a function of transient duration and peak flux. These simulations allow for simulating a wide variety of scenarios including observations with varying sensitivities and durations, multiple overlapping telescope pointings, and a wide variety of light curve shapes with the user having the ability to easily add more. Though the current scientific focus is on the radio regime, the simulations code can be easily adapted to other wavelength regimes.

[ascl:2204.008]
RMNest: Bayesian approach to measuring Faraday rotation and conversion in radio signals

RMNest directly fits the Stokes Q and U (and V) spectra of a radio signal to measure the effects of Faraday rotation (or conversion) induced by propagation through a magnetized plasma along the line of sight. The software makes use of the Bayesian Inference Library (Bilby; ascl:1901.011) as an interface to the dynesty (ascl:1809.013) nested sampling algorithm.

[ascl:2204.009]
MAYONNAISE: ADI data imaging processing pipeline

MAYONNAISE (Morphological Analysis Yielding separated Objects iN Near infrAred usIng Sources Estimation), or MAYO for short, is a pipeline for exoplanet and disk high-contrast imaging from ADI datasets. The pipeline is mostly automated; the package also loads the data and injects synthetic data if needed. MAYONNAISE parameters are written in a json file called parameters_algo.json and placed in a working_directory.

[ascl:2204.010]
FBCTrack: Fragmentation and bulk composition tracking

The fragmentation and bulk composition tracking package contains two codes. The fragmentation code models fragmentation in collisions for the C version of REBOUND (ascl:1110.016). This code requires setting two global parameters. It automatically produces a collision report that details the time of every collision, the bodies involved, how the collision was resolved, and how many fragments were produced; collision outcomes are assigned a numerical value. The bulk composition tracking code tracks the composition change as a function of mass exchange for bodies with a homogenous composition. It is a post-processing code that works in conjunction with the fragmentation code, and requires the collision report generated by the fragmentation code.

[ascl:2204.011]
SimAb: Planet formation model

SimAb (Simulating Abundances) simulates planet formation, focusing on the atmosphere accretion of gas giant planets. The package can run the simulation in two different modes. The single simulation mode is run by specifying the initial conditions (the core mass, the initial orbital distance, the planetesimal ratio, and the dust grain fraction), and the mature planet mass and orbital distance. The multi run simulation mode requires specifying the mass and the final orbital distance of the mature planet; the simulation randomly assigns initial orbital distance, initial core mass, initial planetesimal ratio, and initial dust grain fraction. The package also provides Jupyter codes for plotting the results of the simulations.

[ascl:2204.012]
SpECTRE: Multi-scale, multi-physics problem solver

Deppe, Nils; Throwe, William; Kidder, Lawrence E.; Vu, Nils L.; Hébert, François; Moxon, Jordan; Armaza, Cristóbal; Bonilla, Gabriel S.; Kim, Yoonsoo; Kumar, Prayush; Lovelace, Geoffrey; Macedo, Alexandra; Nelli, Kyle C.; O'Shea, Eamonn; Pfeiffer, Harald P.; Scheel, Mark A.; Teukolsky, Saul A.; Wittek, Nikolas A.; Anantpurkar, Isha; Boyle, Michael; Carpenter, Alexander; Chaudhary, Himanshu; Foucart, Francois; Ghadiri, Noora; Giesler, Matthew; Guo, Jason S.; Iozzo, Dante A. B.; Legred, Isaac; Li, Dongjun; Ma, Sizheng; Melchor, Denyz; Morales, Marlo; Ramirez, Teresita; Rüter, Hannes R.; Sanchez, Jennifer; Thomas, Sierra; Vieira, Daniel; Wlodarczyk, Tom

SpECTRE solves multi-scale, multi-physics problems in astrophysics and gravitational physics, such as those associated with the multi-messenger astrophysics of neutron star mergers, core-collapse supernovae, and gamma-ray bursts. It runs at petascale and is designed for future exascale computers.

[ascl:2204.013]
SCRIPT: Semi-numerical Code for ReIonization with PhoTon-conservation

SCRIPT (Semi-numerical Code for ReIonization with PhoTon-conservation) generates the ionization field during the epoch of cosmological reionization using a photon-conserving algorithm. The code depends on density and velocity files obtained using a N-body simulation, which can be generated with a 2LPT code such as MUSIC (ascl:1311.011).

[ascl:2204.014]
GADGET-4: Parallel cosmological N-body and SPH code

GADGET-4 (GAlaxies with Dark matter and Gas intEracT) is a parallel cosmological N-body and SPH code that simulates cosmic structure formation and calculations relevant for galaxy evolution and galactic dynamics. It is massively parallel and flexible, and can be applied to a variety of different types of simulations, offering a number of sophisticated simulation algorithms. GADGET-4 supports collisionless simulations and smoothed particle hydrodynamics on massively parallel computers.

The code can be used for plain Newtonian dynamics, or for cosmological integrations in arbitrary cosmologies, both with or without periodic boundary conditions. Stretched periodic boxes, and special cases such as simulations with two periodic dimensions and one non-periodic dimension are supported as well. The modeling of hydrodynamics is optional. The code is adaptive both in space and in time, and its Lagrangian character makes it particularly suitable for simulations of cosmic structure formation. Several post-processing options such as group- and substructure finding, or power spectrum estimation are built in and can be carried out on the fly or applied to existing snapshots. Through a built-in cosmological initial conditions generator, it is also particularly easy to carry out cosmological simulations. In addition, merger trees can be determined directly by the code.

[ascl:2204.015]
ADBSat: Aerodynamic Database for Satellites

Sinpetru, Luciana A.; Crisp, Nicholas H.; Roberts, Peter C. E.; Sulliotti-Linner, Valeria; Hanessian, Virginia; Herdrich, Georg H.; Romano, Francesco; Garcia-Almiñana, Daniel; Rodríguez-Donaire, Sílvia; Seminari, Simon; Mostaza-Prieto, David; Livadiotti, Sabrina

ADBSat computes aerodynamic coefficient databases for satellite geometries in free-molecular flow (FMF) conditions. Written in MATLAB, ADBSat imports body geometry from .stl or .obj mesh files, calculates aerodynamic force and moment coefficient for different gas-surface interaction models, and calculates solar radiation pressure force and moment coefficient. It also takes multiple surface and material characteristics into consideration. ADBSat is a panel-method tool that is able to calculate aerodynamic or solar force and moment coefficient sets for satellite geometries by applying analytical (closed-form) expressions for the interactions to discrete flat-plate mesh elements. The panel method of ADBSat assumes FMF conditions. The code analyzes basic shadowing to identify panels that are shielded from the flow by other parts of the body and will therefore not experience any surface interactions. However, this method is dependent on the refinement of the input mesh and can be sensitive to the orientation and arrangement of the mesh elements with respect to the oncoming flow direction.

[ascl:2204.016]
pySIDES: Simulated Infrared Dusty Extragalactic Sky in Python

pySIDES generates mock catalogs of galaxies in the (sub-)millimeter domain and associates spectral cubes (e.g., for intensity mapping experiments). It produces both continuum and CO, [CII], and [CI] line emissions. pySIDES is the Python version of the Simulated Infrared Dusty Extragalactic Sky (SIDES).

[ascl:2204.017]
RSG: Redshift Search Graphs

Redshift Search Graphs provides a fast and reliable way to test redshifts found from sub-mm redshift searches. The scripts can graphically test the robustness of a spectroscopic redshift of a galaxy, test the efficiency of an instrument towards spectroscopic redshift searches, and optimize observations of tunable institutes (such as ALMA) for upcoming redshift searches.

[ascl:2204.018]
ProFuse: Galaxies and components modeler

ProFuse produces physical models of galaxies and their components by combining the functionalities of the source extraction code PROFOUND (ascl:1804.006), the Bayesian galaxy fitting tool ProFit (ascl:1612.004), and the spectral generation package ProSpect (ascl:2002.007). ProFuse uses a self-consistent model for the star formation and metallicity history of the bulge and disk separately to generate images. The package then defines the model likelihood and optimizes the physical galaxy reconstruction using target images across a range of wavelengths.

[ascl:2204.019]
DarkFlux: Dark Matter annihilation spectrum computer

DarkFlux analyzes indirect-detection signatures for next-generation models of dark matter (DM) with multiple annihilation channels. Input is user-generated models with 2 → 2 tree-level dark matter annihilation to pairs of Standard Model (SM) particles. The code analyzes DM annihilation to γ rays using three modules; one computes the fractional annihilation rate, the second computes the total flux at Earth due to DM annihilation, and the third compares the total flux to observational data and computes the upper limit at 95% confidence level (CL) on the total thermally averaged DM annihilation cross section.

[ascl:2204.020]
MonoTools: Planets of uncertain periods detector and modeler

MonoTools detects, vets, and models transiting exoplanets, with a specific emphasis on monotransiting planets and those with unknown periods. It includes scripts specifically for searching and assessing a lightcurve for the presence of monotransits. MonoTools can also performing a best-fit transit model, determine whether transits are linked to any detected multi-transiting planet candidate or with each other, and can fit planets in a Bayesian way to account for uncertain periods, lightcurve gaps, and stellar variability, among other things.

[submitted]
SWIFTGalaxy

SWIFTGalaxy provides a software abstraction of simulated galaxies produced by the SWIFT smoothed particle hydrodynamics code. It extends the SWIFTSimIO module and is tailored to analyses of particles belonging to individual simulated galaxies. It inherits from and extends the functionality of the SWIFTDataset. It understands the output of halo finders and therefore which particles belong to a galaxy, and its integrated properties. The particles occupy a coordinate frame that is enforced to be consistent, such that particles loaded on-the-fly will match e.g. rotations and translations of particles already in memory. Intuitive masking of particle datasets is also enabled. Finally, some utilities to make working in cylindrical and spherical coordinate systems more convenient are also provided.

[submitted]
Python “sgp4” module that offers official SGP4 C++ library

The “sgp4” module is a Python wrapper around the C++ version of the standard SGP4 algorithm for propagating Earth satellite positions from the element sets published by organizations like SpaceTrak and Celestrak. The code is the most recent version, including all of the corrections and bug fixes described in the paper _Revisiting Spacetrack Report #3_ (AIAA 2006-6753) by Vallado, Crawford, Hujsak, and Kelso. The test suite verifies that the Python wrapper returns exactly the coordinates specified in the C++ test cases.

[ascl:2205.001]
PMOIRED: Parametric Modeling of Optical Interferometric Data

PMOIRED models astronomical spectro-interferometric data stored in the OIFITS format. Parametric modeling is used to describe the observed scene as blocks such as disks, rings and Gaussians which can be combined and their parameters linked. It includes plotting, least-square fitting and bootstrapping estimation of uncertainties. For spectroscopic instruments (such as GRAVITY), tools are provided to model spectral lines and correct spectra for telluric lines.

[ascl:2205.002]
am: Microwave through submillimeter-wave propagation tool for the terrestrial atmosphere

am performs optical depth, radiative transfer, and refraction computations involving propagation through the terrestrial atmosphere and other media at microwave through submillimeter wavelengths. The program is used in radio astronomy, atmospheric radiometry, and radio spectrum management.

[ascl:2205.003]
QSOGEN: Model quasar SEDs

The QSOGEN collection of Python code models quasar colors, magnitudes and SEDs. It implements an empirically-motivated parametric model to efficiently account for the observed emission-line properties, host-galaxy contribution, dust reddening, hot dust emission, and IGM suppression in the rest-frame 900-30000A wavelength range for quasars with a wide range of redshift and luminosity.

The code is packaged with a set of empirically-derived emission-line templates and an empirically-derived quasar dust extinction curve which are publicly released.

[ascl:2205.004]
FAlCon-DNS: Framework of time schemes for direct numerical simulation of annular convection

FAlCon-DNS (Framework of time schemes for direct numerical simulation of annular convection) solves for 2-D convection in an annulus and analyzes different time integration schemes. The framework contains a suite of IMEX, IMEXRK and RK time integration schemes. The code uses a pseudospectral method for spatial discretization. The governing equations contain both numerically stiff (diffusive) and non-stiff (advective) components for time discretization. The software offers OpenMP for parallelization.

[ascl:2205.005]
maelstrom: Forward modeling of pulsating stars in binaries

Hey, Daniel R.; Murphy, Simon J.; Foreman-Mackey, Dan; Bedding, Timothy R.; Pope, Benjamin J.S.; Hogg, David W.

maelstrom models binary orbits through the phase modulation technique. This set of custom PyMC3 models and solvers fit each individual datapoint in the time series by forward modeling the time delay onto the light curve. This approach fully captures variations in a light curve caused by an orbital companion.

[ascl:2205.006]
LATTE: Lightcurve Analysis Tool for Transiting Exoplanet

LATTE identifies, vets and characterizes signals in TESS lightcurves to weed out instrumental and astrophysical false positives. The program performs a fast in-depth analysis of targets that have already been identified as promising candidates by the main TESS pipelines or via alternative methods such as citizen science. The code automatically downloads the data products for any chosen TIC ID (short or long cadence TESS data) and produces a number of diagnostic plots that are compiled in a concise report.

[ascl:2205.007]
EarthScatterLikelihood: Event rates and likelihoods for Dark Matter direct detection in the presence of Earth-Scattering

EarthScatterLikelihood calculates event rates and likelihoods for Earth-scattering Dark Matter. It is written in Fortran with plotting routines in Python. For input, it uses results from Monte Carlo simulations generated by DaMaSCUS (ascl:1706.003). It includes routines for submitting many reconstructions in parallel on a cluster, and the properties of the detector, such as for a Germanium and a Sapphire detector, can be edited.

[ascl:2205.008]
Hyperopt: Distributed asynchronous hyper-parameter optimization

The Python library Hyperopt performs serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions. Three algorithms are implemented in hyperopt: Random Search, Tree of Parzen Estimators (TPE), and Adaptive TPE. Algorithms can be parallelized in two ways, using either Apache Spark or MongoDB. To use Hyperopt, the objective function to minimize and the space over which to search, and the database in which to store all the point evaluations of the search have to be described, and the search algorithm to use has to be specified.

[ascl:2205.009]
hyperas: Keras + Hyperopt

Hyperas is a convenience wrapper around hyperopt (ascl:2205.008) for fast prototyping with keras models (ascl:1806.022). Hyperas lets you use the power of hyperopt without having to learn the syntax of it. Instead, just define your keras model as you are used to, but use a simple template notation to define hyper-parameter ranges to tune.

[ascl:2205.010]
pyICs: Initial Conditions creator for isolated galaxy formation simulations

pyICs creates initial condition (IC) files for N-body simulations of the formation of isolated galaxies. It uses the pynbody analysis package (ascl:1305.002) to create the actual IC files. pyICs generates dark matter halos (DM) in dynamical equilibrium which host a rotating gas sphere. The DM particle velocities are drawn from the equilibrium distribution function and the gas sphere has an angular momentum profile. The DM and the gas share the same 3D radial density profile. The code natively supports the αβγ-models: ρ ~ (r/a)-γ[1+(r/a)α](γ-β)/α. If γ <= 3, the profiles are smoothly truncated outside the virial radius. The radial profile can be arbitrary as long as python functions for the profile itself and its first and second derivative with radius are given.

[ascl:2205.011]
myRadex: Radex with a twist

myRadex solves essentially the same problem as RADEX (ascl:1010.075), except that it takes a different approach to solve the statistical equilibrium problem. Given an initial distribution, myRadex evolves the system towards equilibrium using an ODE solver. Frequencies in the input file are used by default, and a function for calculating critical densities of all the transitions of a molecule is included.

[ascl:2205.012]
Zelda: Generate correlation functions and power spectra from a galaxy catalog

The Zelda command-line tool extracts correlation functions in velocity space from a galaxy catalog. Zelda is modular, extendable, and can be generalized to produce power spectra and to work in position space. Written in C, it was heavily inspired by the cosmological Boltzmann code CLASS (ascl:1106.020). Zelda is a parallel code using the OpenMP standard.

[ascl:2205.013]
ld-exosim: Simulate biases using different limb darkening laws

ld-exosim selects the optimal (*i.e.* best estimator in a MSE sense) limb-darkening law for a given transiting exoplanet lightcurve and calculates the limb-darkening induced biases on various exoplanet parameters. Limb-darkening laws include linear, quadratic, logarithmic, square-root and three-parameter laws.

[ascl:2205.014]
FHD: Fast Holographic Deconvolution

Sullivan, Ian; Barry, Nichole; Byrne, Ruby L.; Morales, Miguel F.; Hazelton, Bryna; Beardsley, Adam; Lanman, Adam

FHD is an open-source imaging algorithm for radio interferometers and is written in IDL. The three main use-cases for FHD are efficient image deconvolution for general radio astronomy, fast-mode Epoch of Reionization analysis, and simulation. FHD inputs beam models, calibration files, and sky model catalogs and requires input data to be in uvfits format.

[ascl:2205.015]
CS-ROMER: Compressed Sensing ROtation MEasure Reconstruction

CS-ROMER (Compressed Sensing ROtation MEasure Reconstruction) is a compressed sensing reconstruction framework for Faraday depth spectra. It can simulation Faraday depth sources, subtract Galactic RM, and reconstruct Faraday depth sources from linearly polarized data and Faraday depth sources using Compressed Sensing.

[ascl:2205.016]
Pryngles: PlanetaRY spaNGLES

Pryngles produces visualizations of the geometric configuration of a ringed exoplanet (an exoplanet with a ring or exoring for short) and calculates the light-curve signatures produced by these kind of planets. The model behind the package has been developed in an effort to predict the signatures that exorings may produce not only in the light-curve of transiting exoplanets (a problem that has been extensively studied) but also in the light of stars having non-transiting exoplanets.

[ascl:2205.017]
LiSA: LIghtweight Source finding Algorithms for analysis of HI spectral data

The LIghtweight Source finding Algorithms (LiSA) library finds HI sources in next generation radio surveys. LiSA can analyze input data cubes of any size with pipelines that automatically decompose data into different domains for parallel distributed analysis. For source finding, the library contains python modules for wavelet denoising of 3D spatial and spectral data, and robust automatic source finding using null-hypothesis testing. The source-finding algorithms all have options to automatically choose parameters, minimizing the need for manual fine tuning. Finally, LiSA also contains neural network architectures for classification and characterization of 3D spectral data.

[ascl:2205.018]
ASOHF: Adaptive Spherical Overdensity Halo Finder

ASOHF (Adaptive Spherical Overdensity Halo Finder) identifies bound dark matter structures (dark matter haloes) in the outputs of cosmological simulations, and works directly on an input particle list. The computational cost of running ASOHF in simulations with a large number of particles can be reduced by using a domain decomposition to split the simulation box into smaller boxes, or subdomains, which are then processed independently. The basic output of ASOHF is a halo catalog. The package includes a python code to build a merger tree from ASOHF outputs.

[ascl:2205.019]
HOPS: Haystack Observatory Postprocessing System

Whitney, A. R.; Cappallo, R.; Aldrich, W.; Anderson, B.; Bos, A.; Casse, J.; Goodman, J.; Parsley, S.; Pogrebenko, S.; Schilizzi, R.; Smythe, D.

HOPS (Haystack Observatory Postprocessing System) analyzes the data generated by DiFX VLBI correlators. It is written in C for Linux computers, and emphasizes quality-control aspects of data processing. It sits between the correlator and an image-processing and/or geodetic-processing package, and performs basic fringe-fitting, data editing, problem diagnosis, and correlator support functions.

[ascl:2205.020]
ASTROMER: Building light curves embeddings using transfomers

ASTROMER is a Transformer-based model trained on millions of stars for the representation of light curves. Pretrained models can be directly used or finetuned on specific datasets. ASTROMER is useful in downstream tasks in which data are limited to train deep learning models.

[ascl:2205.021]
CPNest: Parallel nested sampling

CPNest performs Bayesian inference using the nested sampling algorithm. It is designed to be simple for the user to provide a model via a set of parameters, their bounds and a log-likelihood function. An optional log-prior function can be given for non-uniform prior distributions. The nested sampling algorithm is then used to compute the marginal likelihood or evidence. As a by-product the algorithm produces samples from the posterior probability distribution. The implementation is based on an ensemble MCMC sampler which can use multiple cores to parallelize computation.

[ascl:2205.022]
BANG: BAyesian decomposiotioN of Galaxies

Rigamonti, Fabio; Dotti, Massimo; Covino, Stefano; Haardt, Francesco; Landoni, Marco; Del Pozzo, Walter; Lupi, Alessandro; Zibetti, Stefano

BANG (BAyesian decomposiotioN of Galaxies) models both the photometry and kinematics of galaxies. The underlying model is the superposition of different components with three possible combinations: 1.) Bulge + inner disc + outer disc + Halo; 2.) Bulge + disc + Halo; and 3.) inner disc + outer disc + Halo. As CPU parameter estimation can take days, running BANG on GPU is recommended.

[ascl:2205.023]
PyWPF: Waterfall Principal Component Analysis (PCA) Folding

Cassanelli, Tomas; Naletto, Giampiero; Codogno, Giacomo; Barbieri, Cesare; Verroi, Enrico; Zampieri, Luca

PyWPF (Waterfall Principal Component Analysis Folding) finds periodicity in one-dimensional timestream data sets; it is particularly designed for very high noise situations where traditional methods may fail. Given a timestream, with each point being the arrival times of a source, the software computes the estimated period. The core function of the package requires several initial parameters to run, and using the best known period of the source (T_init) is recommended.

[ascl:2205.024]
MM-LSD: Multi-Mask Least-Squares Deconvolution

Lienhard, F.; Mortier, A.; Buchhave, L.; Collier Cameron, A.; López-Morales, M.; Sozzetti, A.; Watson, C. A.; Cosentino, R.

MM-LSD (Multi-Mask Least-Squares Deconvolution) performs continuum normalization of 2D spectra (echelle order spectra). It also masks and partially corrects telluric lines and extracts RVs from spectra. The code requires RASSINE (ascl:2102.022) and uses spectral line data from VALD3.

[ascl:2205.025]
simulateSearch: High-time resolution data sets simulations for radio telescopes

simulateSearch simulates high time-resolution data in radio astronomy. The code is built around producing multiple binary data files that contain information on the radiometer noise and sources that are being simulated. These binary data files subsequently get combined and output PSRFITS

search mode files produced. The PSRFITS files can be processed using standard pulsar software packages such as PRESTO (ascl:1107.017).

[ascl:2206.001]
vortex: Helmholtz-Hodge decomposition for an AMR velocity field

vortex performs a Helmholtz-Hodge decomposition on vector fields defined on AMR grids, decomposing a vector field in its solenoidal (divergence-less) and compressive (curl-less) parts. It works natively on vector fields defined on Adaptive Mesh Refinement (AMR) grids, so that it can perform the decomposition over large dynamical ranges; it is also applicable to particle-based simulations. As vortex is devised primarily to investigate the properties of the turbulent velocity field in the Intracluster Medium (ICM), it also includes routines for multi-scale filtering the velocity field.

[ascl:2206.002]
TCF: Transit Comb Filter periodogram

TCF calculates a periodogram designed to detect exoplanet transits after the light curve has been differenced. It is a matched filter for a periodic double-spike pattern. The difference operator that can be used independently for detrending a light curve; it is also embedded in ARIMA (autoregressive integrated moving average) Box-Jenkins modeling.

[submitted]
Green Bank Observatory Gridder

A stand-alone spectral gridder and imager for the Green Bank Telescope, as well as functionality for any diameter telescope. Based around the cygrid package from Benjamin Winkel and Daniel Lenz

[ascl:2206.003]
ExoJAX: Spectrum modeling of exoplanets and brown dwarfs

Kawahara, Hajime; Kawashima, Yui; Masuda, Kento; Crossfield, Ian; van den Bekerom, Dirk; Kitzmann, Daniel; Morris, Brett; Pannier, Erwan; Nugroho, Stevanus; Ishikawa,Tako

ExoJAX provides auto-differentiable line-by-line spectral modeling of exoplanets/brown dwarfs/M dwarfs using JAX (ascl:2111.002). In a nutshell, ExoJAX allows the user to do a HMC-NUTS fitting using the latest molecular/atomic data in ExoMol, HITRAN/HITEMP, and VALD3. The code enables a fully Bayesian inference of the high-dispersion data to fit the line-by-line spectral computation to the observed spectrum, from end-to-end (i.e. from molecular/atomic databases to real spectra), by combining it with the Hamiltonian Monte Carlo in recent probabilistic programming languages such as NumPyro.

[ascl:2206.004]
pystortion: Distortion measurement support

pystortion provides support for distortion measurements in astronomical imagers. It includes classes to support fitting of bivariate polynomials of arbitrary degree and helper functions for crossmatching catalogs. The crossmatching uses an iterative approach in which a two-dimensional distortion model is fit at every iteration and used to continuously refine the position of extracted sources.

[ascl:2206.005]
NonnegMFPy: Nonnegative Matrix Factorization with heteroscedastic uncertainties and missing data

NonnegMFPy solves nonnegative matrix factorization (NMF) given a dataset with heteroscedastic uncertainties and missing data with a vectorized multiplicative update rule; this can be used create a mask and iterate the process to exclude certain new data by updating the mask. The code can work on multi-dimensional data, such as images, if the data are first flattened to 1D.

[ascl:2206.006]
MYRaf: Aperture photometry GUI for IRAF

MYRaf is a practicable astronomical image reduction and photometry software and interface for IRAF (ascl:9911.002). The library uses IRAF, PyRAF (ascl:1207.011), Ginga (ascl:1303.020), and other python packages with a Qt framework for automated software processing of data from robotic telescopes.

[ascl:2206.007]
CircleCraters: Crater-counting plugin for QGIS

CircleCraters is a projection independent crater counting plugin for QGIS. It has the flexibility to crater count in a GIS environment on Windows, OS X, or Linux, and uses three-click input to define crater rims as a circle.

[ascl:2206.008]
Craterstats2: Planetary surface dating from crater size-frequency distribution measurements

Craterstats2 plots crater counts and determining surface ages. The software plots isochrons in cumulative, differential, R-plot and Hartmann presentations, and makes isochron fits to both cumulative and differential data. Hartmann-style piecewise production functions may also be used. A Python implementation of the software, Craterstats3, is also available.

[ascl:2206.009]
Craterstats3: Analyze and plot crater count data for planetary surface dating

Craterstats3 analyzes and plots crater count data for planetary surface dating. It is a Python implementation of Craterstats2 (ascl:2206.008) and is designed to replicate the output of the previous version as closely as possible. As before, it produces plots in cumulative, differential, Hartmann, and R-plot styles with possible overlays of crater counts, isochrons, equilibrium functions and epoch boundaries, as well aschronology and impact rate functions. Data can be shown with various binnings or unbinned, and age estimates made by either cumulative fitting, differential fitting, or Poisson timing evaluation. Numerical results can be output as text for further processing elsewhere. A number of published chronology systems are already set up for use, but new ones may be added by the user. The software is designed to be easily integrated into other software, which could allow the addition of a graphical interface or the inclusion of some Craterstats functions into a GIS.

[ascl:2206.010]
pyHIIexplorerV2: Integrated spectra of HII regions extractor

Espinosa-Ponce, C.; Sánchez, S. F.; Morisset, C.; Barrera-Ballesteros, J. K.; Galbany, L.; García-Benito, R.; Lacerda, E. A. D.; Mast, D.

pyHIIexplorerV2 extracts the integrated spectra of HII regions from integral field spectroscopy (IFS) datacubes. The detection of HII regions performed by pyHIIexplorer is based on two assumptions: 1) HII regions have strong emission lines that are clearly above the continuum emission and the average ionized gas emission across each galaxy, and 2) the typical size of HII regions is about a few hundreds of parsecs, which corresponds to a usual projected size of a few arcsec at the distance of our galaxies. These assumptions will define clumpy structures with a high Ha emission line contrast in comparison to the continuum. pyHIIexplorerV2 is written in Python; it is based on and is a successor to HIIexplorer (ascl:1603.017).

[submitted]
fastrometry: Fast world coordinate solution solver

Fastrometry is a Python implementation of the fast world coordinate solution solver for the FITS standard astronomical image. When supplied with the approximate field center (+-25%) and the approximate field scale (+-10%) of the telescope and detector system the astronomical image is from, fastrometry provides WCS solutions almost instantaneously. The algorithm is also originally implemented with parallelism enabled in the Windows FITS image processor and viewer CCDLAB (ascl:2206.021).

[submitted]
JPFITS (C# .Net FITS File Interaction)

FITS File interaction written in Visual Studio C# .Net.

JPFITS is not based upon any other implementation and is written from the ground-up, consistent with the FITS standard, designed to interact with FITS files as object-oriented structures.

JPFITS provides functionality to interact with FITS images and binary table extensions, as well as providing common mathematical methods for the manipulation of data, data reductions, profile fitting, photometry, etc.

JPFITS also implements object-oriented classes for Point Source Extraction, World Coordinate Solutions (WCS), WCS automated field solving, FITS Headers and Header Keys, etc.

The automatic world coordinate solver is based on the trigonometric algorithm as described here:

https://iopscience.iop.org/article/10.1088/1538-3873/ab7ee8

All function parameters, methods, properties, etc., are coded with XML descriptions which will function with Visual Studio. Other code editors may or may not read the XML files.

Everything which is reasonable to parallelize in order to benefit from the computation speed increase for multi-threaded systems has been done so. In all such cases function options are given in order to specify the use of parallelism or not. Generally, most image manipulation functions are highly amenable to parallelism. No parallelism is forced, i.e., any code which may execute parallelized is given a user option to do so or not.

[ascl:2206.011]
IFSCube: Analyze and process integral field spectroscopy data cubes

IFSCube performs analysis tasks in data cubes of integral field spectroscopy. It contains routines for fitting spectral features in 1D spectra and data cubes and rotation models to velocity fields; it also contains a routine that inspects the fit results. Though originally intended to make user scripts more concise, analysis can also be performed on the fly by using an interactive interpreter such as ipython. By default, IFSCube assumes data are in the Flexible Image Transport System (FITS) standard, but the package can be modified easily to allow use of other data formats.

[ascl:2206.012]
WDPhotTools: White Dwarf Photometric SED fitter and luminosity function builder

WDPhotTools generates color-color diagrams and color-magnitude diagrams in various photometric systems, plots cooling profiles from different models, and computes theoretical white dwarf luminosity functions based on the built-in or supplied models of the (1) initial mass function, (2) total stellar evolution lifetime, (3) initial-final mass relation, and (4) white dwarf cooling time. The software has three main parts: the formatters that handle the output models from various works in the format as they are downloaded; the photometric fitter that solves for the WD parameters based on the photometry, with or without distance and reddening; and the generator of the white dwarf luminosity function in bolometric magnitudes or in any of the photometric systems available from the atmosphere model.

[ascl:2206.013]
smooth: Smoothing for N-body simulations

Smooth calculates several mean quantities for all particles in an N-Body simulation output file. The program produces a file for each type of output specified on the command line. This output file is in ASCII format with one smoothed quantity for each particle. The program uses a symmetric SPH (Smoothed Particle Hydrodynamics) smoothing kernel to find the mean quantities.

[ascl:2206.014]
SpinSpotter: Stellar rotation periods from high-cadence photometry calculator

SpinSpotter calculates stellar rotation periods from high-cadence photometry. The code uses the autocorrelation function (ACF) to identify stellar rotation periods up to one-third the observational baseline of the data. SpinSpotter includes diagnostic tools that describe features in the ACF and allows tuning of the tolerance with which to accept a period detection.

[ascl:2206.015]
Smart: Automatic differentiation of accelerations and variational equations

Smart provides pre-processing for LP-VIcode (ascl:1501.007). It computes the accelerations and variational equations given a generic user-defined potential function, eliminating the need to calculate manually the accelerations and variational equations.

[ascl:2206.016]
wdwarfdate: White dwarfs age calculator

wdwarfdate derives the Bayesian total age of a white dwarf from an effective temperature and a surface gravity. It runs a chain of models assuming single star evolution and estimates the following parameters and their uncertainties: total age of the object, mass and cooling age of the white dwarf, and mass and lifetime of the progenitor star.

[ascl:2206.017]
atoMEC: Average-Atom code for Matter under Extreme Conditions

Callow, Timothy; Kotik, Daniel; Tsvetoslavova Stankulova, Ekaterina; Rahat, Nathan; Kraisler, Eli; Cangi, Attila

atoMEC simulates high energy density phenomena such as in warm dense matter. It uses Kohn-Sham density functional theory, in combination with an average-atom approximation, to solve the electronic structure problem for single-element materials at finite temperature.

[ascl:2206.018]
MADYS: Isochronal parameter determination for young stellar and substellar objects

MADYS (Manifold Age Determination for Young Stars) determines the age and mass of young stellar and substellar objects. The code automatically retrieves and cross-matches photometry from several catalogs, estimates interstellar extinction, and derives age and mass estimates for individual objects through isochronal fitting. MADYS harmonizes the heterogeneity of publicly-available isochrone grids and the user can choose amongst several models, some of which have customizable astrophysical parameters. Particular attention has been dedicated to the categorization of these models, labeled through a four-level taxonomical classification.

[ascl:2206.019]
SEVN: Stellar EVolution for N-body

The population synthesis code SEVN (Stellar EVolution for N-body) includes up-to-date stellar evolution (through look-up tables), binary evolution, and different recipes for core-collapse supernovae. SEVN also provides an up-to-date formalism for pair-instability and pulsational pair-instability supernovae, and is designed to interface with direct-summation N-body codes such as STARLAB (ascl:1010.076) and HiGPUs (ascl:1207.002).

[ascl:2206.020]
CCDLAB: FITS image viewer and data reducer

CCDLAB provides graphical user interface functionality for FITS image viewing and data reduction based on the JPFITS FITS-file interface. It can view, manipulate, and save FITS primary image data and image extensions, view and manipulate FITS image headers, and view FITS Bintable extensions. The code enables batch processing, viewing, and saving of FITS images and searching FITS files on disk. CCDLAB also provides general image reduction techniques, source detection and characterization, and can create World Coordinate Solutions automatically or manually for FITS images.

[ascl:2206.021]
PyCASSO2: Stellar population and emission line fits in integral field spectra

de Amorim, André Luiz; Vale Asari, Natalia; Cid Fernandes, Roberto; García-Benito, Rubén; Werle, Ariel

PyCASSO runs the STARLIGHT code (ascl:1108.006) in integral field spectra (IFS). Cubes from various instruments are supported, including PMAS/PPAK (CALIFA), MaNGA, GMOS and MUSE. Emission lines can be measured using DOBBY, which is included in the package. The package also includes tools for IFS cubes analysis and plotting.

[ascl:2206.022]
RealSim-IFS: Realistic synthetic integral field spectrscopy of galaxies from numerical simulations

RealSim-IFS generates survey-realistic integral-field spectroscopy (IFS) observations of galaxies from numerical simulations of galaxy formation. The tool is designed primarily to emulate current and experimental observing strategies for IFS galaxy surveys in astronomy, and can reproduce both the flux and variance propagation of real galaxy spectra to cubes. RealSim-IFS has built-in functions supporting SAMI and MaNGA IFU footprints, but supports any fiber-based IFU design, in general.

[ascl:2206.023]
pyPipe3D: Spectroscopy analysis pipeline

Lacerda, Eduardo A. D.; Sánchez, S. F.; Mejía-Narváez, A.; Camps-Fariña, A.; Espinosa-Ponce, C.; Barrera-Ballesteros, J. K.; Ibarra-Medel, H.; Lugo-Aranda, A. Z.

The spectroscopy analysis pipeline pyPipe3D produces coherent and easy to distribute and compare parameters of stellar populations and ionized gas; it is suited in particular for data from the most recent optical IFS surveys. The pipeline is build using pyFIT3D, which is the main spectral fitting module included in this package.

[ascl:2206.024]
Wavetrack: Arbitrary time-evolving solar object recognition and tracking

Wavetrack recognizes and tracks CME shock waves, filaments, and other solar objects. The code creates base images by averaging а series of images a few minutes prior to the start of the eruption and constructs base difference images by subtracting base images from the current raw image of the sequence. This enhances the change in intensity caused by coronal bright fronts, omits static details, and reduces noise. Wavetrack then chooses an appropriate intensity interval and decomposes the base difference or running difference image with an A-Trous wavelet transform, where each wavelet coefficient is obtained by convolving the image array with a corresponding iteration of the wavelet kernel. When the maximum value of the wavelet coefficients for a connected set of pixels satisfies certain conditions, this region is considered as a structure on the respective wavelet coefficient. Separate stand-alone object masks are obtained with a clustering algorithm and objects are renumbered according to the number of the quadrant they belong at each iteration.

[ascl:2206.025]
CuspCore: Core formation in dark matter haloes and ultra-diffuse galaxies by outflow episodes

Freundlich, Jonathan; Jiang, Fangzhou; Dekel, Avishai; Cornuault, Nicolas; Ginzburg, Omry; Koskas, Rémy; Lapiner, Sharon; Dutton, Aaron; Macciò, Andrea V.

CuspCore describes the formation of flat cores in dark matter haloes and ultra-diffuse galaxies from feedback-driven outflow episodes. The halo response is divided into an instantaneous change of potential at constant velocities followed by an energy-conserving relaxation. The core assumption of the model is that the total energy E=U+K is conserved for each shell enclosing a given dark matter mass, which is treated in the code as a least-square minimization of the difference between the final and the initial energy of each shell.

[ascl:2206.026]
ShapePipe: Galaxy shape measurement pipeline

Farrens, S.; Guinot, A.; Kilbinger, M.; Liaudat, T.; Baumont, L.; Jimenez, X.; Peel, A.; Pujol, A.; Schmitz, M.; Starck, J. -L.; Vitorelli, A. Z.

ShapePipe processes single-exposure images and stacked images. Input images have to be calibrated beforehand for astrometry and photometry. The code can handle different image and file types, such as single-exposure mosaic, single-exposure single-CCD, stacked images, database catalog files, and PSF files, some of which are created by the pipeline during the analysis, among others. The end product of ShapePipe is a final catalog containing information for each galaxy, including its shape parameters and the ellipticity components :math:e_1 and :math:e_2. This catalog also contains shapes of artificially sheared images. This information is used in post-processing to compute calibrated shear estimates via metacalibration.

[ascl:2206.027]
DustFilaments: Paint filaments to produce a thermal dust full sky map at mm frequencies

DustFilaments paints filaments in the Celestial Sphere to generate a full sky map of the Thermal Dust emission at millimeter frequencies by integrating a population of 3D filaments. The code requires a magnetic field cube, which can be calculated separately or by DustFilaments. With the magnetic field cube as input, the package creates a random filament population with a given seed, and then paints a filament into a healpix map provided as input; the healpix map is updated in place.

[ascl:2206.028]
Spritz: General relativistic magnetohydrodynamic code

The Spritz code is a fully general relativistic magnetohydrodynamic code based on the Einstein Toolkit (ascl:1102.014). The code solves the GRMHD equations in 3D Cartesian coordinates and on a dynamical spacetime. Spritz supports tabulated equations of state, takes finite temperature effects into account and allows for the inclusion of neutrino radiation.

[submitted]
Compact Binary Chebyshev Polynomial Representation Ephemeris Kernel

The software used to transform the tabular USNO/AE98 asteroid ephemerides into a Chebyshev polynomial representations, and evaluate them at an arbitrary time is available. The USNO/AE98 consisted of the ephemerides of fifteen of the largest asteroids, and were used in The Astronomical Almanac from 2000 through 2015. These ephemerides are outdated and no longer available, but the software used to store and evaluate them is still available and provides a robust method for storing compact ephemerides of solar system bodies.

The object of the software is to provide a compact binary representation of solar system bodies with eccentric orbits, which can produce the body's position and velocity at an arbitrary instant within the ephemeris' time span. It uses a modification of the Newhall (1989) algorithm to achieve this objective. The Newhall algorithm is used to store both the Jet Propulsion Laboratory DE and the Institut de mécanique céleste et de calcul des éphémérides INPOP high accuracy planetary ephemerides. The Newhall algorithm breaks an ephemeris into a number time contiguous segments, and each segment is stored as a set of Chebyshev polynomial coefficients. The length of the time segments and the maximum degree Chebyshev polynomial coefficient is fixed for each body. This works well for bodies with small eccentricities, but it becomes inefficient for a body in a highly eccentric orbit. The time segment length and maximum order Chebyshev polynomial coefficient must be chosen to accommodate the strong curvature and fast motion near pericenter, while the body spends most of its time either moving slowly near apocenter or in the lower curvature mid-anomaly portions of its orbit. The solution is to vary the time segment length and maximum degree Chebyshev polynomial coefficient with the body's position. The portion of the software that converts tabular ephemerides into a Chebyshev polynomial representation (CPR) performs this compaction automatically, and the portion that evaluates that representation requires only a modest increase in the evaluation time.

The software also allows the user to choose the required tolerance of the CPR. Thus, if less accuracy is required a more compact, somewhat quicker to evaluate CPR can be manufactured and evaluated. Numerical tests show that a fractional precision of 4e-16 may be achieved, only a factor of 4 greater than the 1e-16 precision of a 64-bit IEEE (2019) compliant floating point number.

The software is written in C and designed to work with the C edition of the Naval Observatory Vector Astrometry Software (NOVAS). The programs may be used to convert tabular ephemerides of other solar system bodies as well. The included READ.ME file provides the details of the software and how to use it.

REFERENCES

IEEE Computer Society 2019, IEEE Standard for Floating-Point Arithmetic. IEEE STD 754-2019, IEEE, pp. 1–84

Newhall, X X 1989, 'Numerical Representation of Planetary Ephemerides,' Celest. Mech., 45, 305 - 310

[submitted]
CosmicEmu: High Precision Emulator for the Nonlinear Matter Power Spectrum

Moran, Kelly R.; Heitmann, Katrin; Lawrence, Earl; Habib, Salman; Bingham, Derek; Upadhye, Amol; Kwan, Juliana; Higdon, David; Payne, Richard

Modern cosmological surveys are delivering datasets characterized by unprecedented quality and statistical completeness. In order to maximally extract cosmological information from these observations, matching theoretical predictions are needed. In the nonlinear regime of structure formation, cosmological simulations are the primary means of obtaining the required information but the computational cost of sufficiently resolved large-volume simulations makes it prohibitive to run very large ensembles. Nevertheless, precision emulators built on a tractable number of high-quality simulations can be used to build very fast prediction schemes to enable a variety of cosmological inference studies. The "Mira-Titan Universe" simulation suite covers the standard six cosmological parameters and, in addition, includes massive neutrinos and a dynamical dark energy equation of state. It is based on 111 cosmological simulations, each covering a (2.1Gpc)^3 volume and evolving 3200^3 particles, and augments these higher-resolution simulations with an additional set of 1776 lower-resolution simulations and TimeRG perturbation theory results to cover scales straddling the linear to mildly nonlinear regimes. The emulator built on this suite, the CosmicEmu, provides predictions at the two to three percent level of accuracy over a wide range of cosmological parameters. Presented in: https://arxiv.org/abs/2207.12345.

[ascl:2207.001]
MULTIGRIS: Multicomponent probabilistic grid search

MULTIGRIS (also called mgris) uses the sequential Monte Carlo method in PyMC (ascl:1506.005) to extract the posterior distributions of primary grid parameters and predict unobserved parameters/observables. The code accepts either a discrete number of components and/or continuous (e.g., power-law, normal) distributions for any given parameter. MULTIGRIS, written in Python, infers the posterior probability functions of parameters in a multidimensional potentially incomplete grid with some observational tracers defined for each parameter set. Observed values and their potentially asymmetric uncertainties are used to calculate a likelihood which, together with predefined or custom priors, produces the posterior distributions. Linear combinations of parameter sets may be used with inferred mixing weights and nearest neighbor or linear interpolation may be used to sample the parameter space.

[ascl:2207.002]
pynucastro: Python interfaces to the nuclear reaction rate databases

pynucastro interfaces to the nuclear reaction rate databases, including the JINA Reaclib nuclear reactions database. This set of Python interfaces enables interactive exploration of rates and collection of rates (networks) in Jupyter notebooks and easy creation of the righthand side routines for reaction network integration (the ODEs) for use in simulation codes.

[ascl:2207.003]
MeSsI: MErging SystemS Identification

MeSsI performs an automatic classification between merging and relaxed clusters. This method was calibrated using mock catalogues constructed from the millennium simulation, and performs the classification using some machine learning techniques, namely random forest for classification and mixture of gaussians for the substructure identification.

[ascl:2207.004]
cosmic-kite: Auto-encoding the Cosmic Microwave Background

Cosmic-kite performs a fast estimation of the TT Cosmic Microwave Background (CMB) power spectra corresponding to a set of cosmological parameters; it can also estimate the maximum-likelihood cosmological parameters from a power spectra. This software is an auto-encoder that was trained and calibrated using power spectra from random cosmologies computed with the CAMB code (ascl:1102.026).

[ascl:2207.005]
echelle: Dynamic echelle diagrams for asteroseismology

Echelle diagrams are used mainly in asteroseismology, where they function as a diagnostic tool for estimating Δν, the separation between modes of the same degree ℓ; the amplitude spectrum of a star is stacked in equal slices of Δν, the large separation. The echelle Python code creates and manipulates echelle diagrams. The code provides the ability to dynamically change Δν for rapid identification of the correct value. echelle features performance optimized dynamic echelle diagrams and multiple backends for supporting Jupyter or terminal usage.

[ascl:2207.006]
MultiModes: Efficiently analyze pulsating stars

Pamos Ortega, David; García Hernández, Antonio; Suárez, Juan Carlos; Pascual Granado, Javier; Barceló Forteza, Sebastià; Rodón, José Ramón

MultiModes extracts the most significant frequencies of a sample of classical pulsating stars. The code takes a directory with light curves and initial parameters as input. For every light curve, the code calculates the frequencies spectrum, or periodogram, with the Fast Lomb Scargle algorithm, extracts the higher amplitude peak, and evaluates whether it is a real signal or noise. It fits frequency, amplitude, and phase through non-linear optimization, using a multisine function. This function is redefined with the new calculated parameters. MultiModes then does a simultaneous fit of a number of peaks (20 by default), subtracts them from the original signal, and goes back to the beginning of the loop with the residual, repeating the same process until the stop criterion is reached. After that, the code can filter suspicious spurious frequencies, those of low amplitude below the Rayleigh resolution, and possible combined frequencies.

[ascl:2207.007]
Pyriod: Period detection and fitting routines

Pyriod provides basic period detection and fitting routines for astronomical time series. Written in Python and designed to be run interactively in a Jupyter notebook, it displays and allows the user to interact with time series data, fit frequency solutions, and save figures from the toolbar. It can display original or residuals time series, fold the time series on some frequency, add selected peaks from the periodogram to the model, and refine the fit by computing a least-squared fit of the model using Lmfit (ascl:1606.014).

[ascl:2207.008]
TESS_PRF: Display the TESS pixel response function

TESS_PRF displays the TESS pixel response function (PRF) at any location on the detector. The package is primarily for estimating how the light from a point source is distributed given its position in a TESS Target Pixel File (TPF) or TESScut postage stamp. By default, it accesses the relevant PRF files on MAST, but can also reference files on a local directory. TESS_PRF assumes the PRF doesn't change considerably within a small TPF. The PRF model can be positioned by passing the relative row and column location within the TPF to the "resample" method. The pixel locations follow WCS convention, that an integer value corresponds to the center of a pixel.

[ascl:2207.009]
SolAster: 'Sun-as-a-star' radial velocity variations

Ervin, Tamar; Halverson, Samuel; Burrows, Abigail; Murphy, Nei; Roy, Arpita; Haywood, Raphaelle D.; Rescigno, Federica; Bender, Chad F.; Lin, Andrea S. J.; Burt, Jennifer; Mahadevan, Suvrath

SolAster provides querying, analysis, and calculation methods to independently derive 'sun-as-a-star' RV variations using SDO/HMI data for any time span since SDO has begun observing. Scaling factors are provided in order to calculate RVs comparable to magnitudes measured by ground-based spectrographs (HARPS-N and NEID). In addition, there are routines to calculate magnetic observables to compare with RV variations and determine what is driving Solar activity.

[ascl:2207.010]
Helios-r2: Bayesian nested-sampling retrieval code

Helios-r2 performs atmospheric retrieval of brown dwarf and exoplanet spectra. It uses a Bayesian statistics approach by employing a nested sampling method to generate posterior distributions and calculate the Bayesian evidence. The nested sampling itself is done by Multinest (ascl:1109.006). The computationally most demanding parts of the model have been written in NVIDIA's CUDA language for an increase in computational speed. Successful applications include retrieval of brown dwarf emission spectra and secondary eclipse measurements of exoplanets.

[ascl:2207.011]
samsam: Scaled Adaptive Metropolis SAMpler

The samsam package provides two samplers, a scaled adaptive metropolis algorithm to robustly obtain samples from a target distribution, and a covariance importance sampling algorithm to efficiently compute the model evidence (or other integrals). It also includes tools to assess the convergence of the sam sampler and a few commonly used prior distributions.

[ascl:2207.012]
ExoCTK: Exoplanet Characterization Tool Kit

Bourque, Matthew; Espinoza, Néstor; Filippazzo, Joseph; Fix, Mees; King, Teagan; Martlin, Catherine; Medina, Jennifer; Batalha, Natasha; Fox, Michael; Fowler, Jules; Fraine, Jonathan; Hill, Matthew; Lewis, Nikole; Stevenson, Kevin; Valenti, Jeff; Wakeford, Hannah

The Exoplanet Characterization ToolKit (ExoCTK) focuses primarily on the atmospheric characterization of exoplanets and provides tools for time-series observation planning, forward modeling, data reduction, limb darkening, light curve fitting, and retrievals. It contains calculators for contamination, visibility, integrations and groups, and includes several Jupyter Notebooks to aid in learning how to use the various tools included in the ExoCTK package.

[ascl:2207.013]
MuSCAT2_transit_pipeline: MuSCAT2 photometry and transit analysis pipelines

MuSCAT2_transit_pipeline provides photometry and transit analysis pipelines for MuSCAT2. It consists of a set of executable scripts and two Python packages: muscat2ph for photometry, and muscat2ta for transit analysis. The MuSCAT2 photometry can be carried out using the scripts only. The transit analysis can also in most cases be done using the main transit analysis script m2fit, but the muscat2ta package also offers high-level classes that can be used to carry out more customized transit analysis as a Python script (or Jupyter notebook).

[ascl:2207.014]
petitRADTRANS: Exoplanet spectra calculator

petitRADTRANS (pRT) calculates transmission and emission spectra of exoplanets for clear and cloudy planets. It also incorporates an easy subpackage for running retrievals with nested sampling. It allows the calculation of emission or transmission spectra, at low or high resolution, clear or cloudy, and includes a retrieval module to fit a petitRADTRANS model to spectral data. pRT has two different opacity treatment modes. The low resolution mode runs calculations at λ/Δλ ≤ 1000 using the so-called correlated-k treatment for opacities. The high resolution mode runs calculations at λ/Δλ ≤ 10^{6}, using a line-by-line opacity treatment.

[ascl:2207.015]
calviacat: Calibrate star photometry by catalog comparison

calviacat calibrates star photometry by comparison to a catalog, including PanSTARRS 1, ATLAS-RefCat2, and SkyMapper catalogs. Catalog queries are cached so that subsequent calibrations of the same or similar fields can be more quickly executed.

[ascl:2207.016]
DustPy: Simulation of dust evolution in protoplanetary disks

DustPy simulates the radial evolution of gas and dust in protoplanetary disks, involving viscous evolution of the gas disk and advection and diffusion of the dust disk, as well as dust growth by solving the Smoluchowski equation. The package provides a standard simulation and the ability to plot results, and also allows modification of the initial conditions for dust, gas, the grid, and the central star.

[ascl:2207.017]
LOTUS: 1D Non-LTE stellar parameter determination via Equivalent Width method

LOTUS (non-LTE Optimization Tool Utilized for the derivation of atmospheric Stellar parameters) derives stellar parameters via Equivalent Width (EW) method with the assumption of 1D non-local thermodynamic equilibrium. It mainly applies on the spectroscopic data from high resolution spectral survey. It can provide extremely accurate measurement of stellar parameters compared with non-spectroscopic analysis from benchmark stars. LOTUS provides a fast optimizer for obtaining stellar parameters based on Differential Evolution algorithm, well constrained uncertainty of derived stellar parameters from slice-sampling MCMC from PyMC3 (ascl:1610.016), and can interpolate the Curve of Growth from theoretical EW grid under the assumptions of LTE and Non-LTE. It also visualizes excitation and ionization balance when at the optimal combination of stellar parameters.

[ascl:2207.018]
pocoMC: Preconditioned Monte Carlo method for accelerated Bayesian inference

pocoMC performs Bayesian inference, including model comparison, for challenging scientific problems. The code utilizes a normalizing flow to precondition the target distribution by removing any correlations between its parameters. pocoMC then generates posterior samples, used for parameter estimation, with a powerful adaptive Sequential Monte Carlo algorithm manifesting a sampling efficiency that can be orders of magnitude higher than without precondition. Furthermore, pocoMC also provides an unbiased estimate of the model evidence that can be used for the task of Bayesian model comparison. The code is designed to excel in demanding parameter estimation problems that include multimodal and highly non–Gaussian target distributions.

[ascl:2207.019]
walter: Predictor for the number of resolved stars in a given observation from RST

Lancaster, Lachlan; Pearson, Sarah; Williams, Benjamin F.; Johnston, Kathryn V.; Starkenburg, Tjitske K.; Kado-Fong, Erin; Seth, Anil C.; Bell, Eric F.

walter calculates the number density of stars detected in a given observation aiming to resolve a stellar population. The code also calculates the exposure time needed to reach certain population features, such as the horizontal branch, and provides an estimate of the crowding limit. walter was written with the expectation that such calculations will be very useful for planning surveys with the Nancy Grace Roman Space Telescope (RST, formerly WFIRST).

[ascl:2207.020]
vKompth: Time-dependent Comptonization model for black-hole X-ray binaries

vKompth fits the energy-dependent rms-amplitude and phase-lag spectra of low-frequency quasi-periodic oscillations in low mass black-hole X-ray binaries using a variable Comptonization model. The accretion disc is modeled as a multi-temperature blackbody source emitting soft photons which are then Compton up-scattered in a spherical corona, including feedback of Comptonized photons that return to the disc.

[ascl:2207.021]
BAYGAUD: BAYesian GAUssian Decomposer

BAYGAUD (BAYesian GAUssian Decomposer) implements the decomposition of velocity profiles in a data cube and subsequent classification. It uses MultiNest (ascl:1109.006) for calculating the posterior distribution and the evidence for a given likelihood function. The code models a given line profile with an optimal number of Gaussians based on the Bayesian Markov Chain Monte Carlo (MCMC) techniques. BAYGAUD is parallelized using the Message-Passing Interface (MPI) standard, which reduces the time needed to calculate the evidence using MCMC techniques.

[ascl:2207.022]
triple-stability: Triple-star system stability determinator

triple-stability uses a simple form of an artificial neural network, a multi-layer perceptron, to check whether a given configuration of a triple-star system is dynamically stable. The code is written in Python and the MLP classifier can be imported to other custom Python3 scripts.

[ascl:2207.023]
MCFOST: Radiative transfer code

Pinte, C.; Ménard, F.; Duchêne, G.; Bastien, P.; Harries, T. J.; Min, M.; Watson, A. M.; Dullemond, C. P.; Woitke, P.; Durán-Rojas, M. C.

MCFOST is a 3D continuum and line radiative transfer code based on an hybrid Monte Carlo and ray-tracing method. It is mainly designed to study the circumstellar environment of young stellar objects, but has been used for a wide range of astrophysical problems. The calculations are done exactly within the limitations of the Monte Carlo noise and machine precision, *i.e.*, no approximation are used in the calculations. The code has been strongly optimized for speed.

MCFOST is primarily designed to study protoplanetary disks. The code can reproduce most of the observations of disks, including SEDs, scattered light images, IR and mm visibilities, and atomic and molecular line maps. As the Monte Carlo method is generic, any complex structure can be handled by MCFOST and its use can be extended to other astrophysical objects. For instance, calculations have succesfully been performed on infalling envelopes and AGB stars. MCFOST also includes a non-LTE line transfer module, and NLTE level population are obtained via iterations between Monte Carlo radiative transfer calculations and statistical equilibrium.

[ascl:2207.024]
pymcfost: Python interface to the MCFOST 3D radiative transfer code

pymcfost provides an interface to and can be used to visualize results from the 3D radiative transfer code MCFOST (ascl:2207.023). pymcfost can set up continuum and line models, read a single model or library of models, plot basic quantities such as density structures and temperature maps, and plot observables, including SEDs, polarization maps, visibilities, and channels maps (with spatial and spectral convolution). It can also convert units (*e.g.* W.m-2 to Jy or brightness temperature), and it provides an interface to the ALMA CASA simulator (ascl:1107.013).

[ascl:2207.025]
casa_cube: Display and analyze astronomical data cubes

casa_cube provides an interface to data cubes generated by CASA (ascl:1107.013) or Gildas (ascl:1305.010). It performs simple tasks such as plotting given channel maps, moment maps, and line profile in various units, and also corrects for cloud extinction, reconvolves with a beam taper, and permits quick and easy comparisons with models.

[ascl:2207.026]
pdspy: MCMC tool for continuum and spectral line radiative transfer modeling

pdspy fits Monte Carlo radiative transfer models for protostellar/protoplanetary disks to ALMA continuum and spectral line datasets using Markov Chain Monte Carlo fitting. It contains two tools, one to fit ALMA continuum visibilities and broadband spectral energy distributions (SEDs) with full radiative transfer models, and another to fit ALMA spectral line visibilities with protoplanetary disk models that include a vertically isothermal, power law temperature distribution. No radiative equilibrium calculation is done.

[ascl:2207.027]
ConeRot: Velocity perturbations extractor

ConeRot extracts velocity perturbations in protoplanetary disks from observed line centroids maps ν∘, by creating axially-symmetric centroid maps. It also derives 3D rotation curves in disk-centered cylindrical coordinates, and can estimate the disk orientation based on line data alone. It approximates the unit opacity surface of an axially symmetric disc by a series of cones whose orientations are fit to the observed velocity centroid in concentric radial domains, or regions, with the disc orientation and the rotation curve both optimized to fit ν∘ in each region. ConeRot extracts the perturbations directly from observations without strong assumptions about the underlying disk model and employs a reduced number of free parameters.

[ascl:2207.028]
disksurf: Measure the molecular emission surface of protoplanetary disks

disksurf measures the height of optically thick emission or photosphere in moderately inclined protoplanetary disks. The package is dependent on AstroPy (ascl:1304.002) and uses GoFish (ascl:2011.016) to retrieve data from FITS data cubes and user-specified parameters to return a surface object containing the disk-centric coordinates of the surface and the gas temperature and rotation velocity at those locations. disksurf provides clipping, smoothing, and diagnostic functions as well.

[ascl:2207.029]
ParticleGridMapper: Particle data interpolator

ParticleGridMapper.jl interpolates particle data onto either a Cartesian (uniform) grid or an adaptive mesh refinement (AMR) grid where each cell contains no more than one particle. The AMR grid can be trimmed with a user-defined maximum level of refinement. Three different interpolation schemes are supported: nearest grid point (NGP), smoothed-particle hydrodynamics (SPH), and Meshless finite mass (MFM). It is multi-threading parallel.

[ascl:2207.030]
Analysis of dipole alignment in large-scale distribution of galaxy spin directions

This code analyzes a dipole axis in the distribution of galaxy spin directions. The code takes as input a list of galaxies, their equatorial coordinates, and their spin directions. It then determines the statistical significance of possible dipole axis at any point in the sky by comparing the cosine dependence of the spin directions to the mean and standard deviation of the cosine dependence after 2000 runs with random spin directions. A code to analyze the binomial distribution of the spin directions using Monte Carlo simulation is also available.

[ascl:2207.031]
BANZAI: Beautiful Algorithms to Normalize Zillions of Astronomical Images

BANZAI (Beautiful Algorithms to Normalize Zillions of Astronomical Images) processes raw data taken from Las Cumbres Observatory and produces science quality data products. It is capable of reducing single or multi-extension fits files. For historical data, BANZAI can also reduce the data cubes that were produced by the Sinistro cameras.

[ascl:2207.032]
gwdet: Detectability of gravitational-wave signals from compact binary coalescences

gwdet computes the probability of detecting a gravitational-wave signal from compact binaries averaging over sky-location and source inclination. The code has two classes, averageangles and detectability. averageangles computes the detection probability, averaged over all angles (such as sky location, polarization, and inclination), as a function of the projection parameter. detectability computes the detection probability of a non-spinning compact binary.

[ascl:2207.033]
piXedfit: Analyze spatially resolved SEDs of galaxies

piXedfit provides a self-contained set of tools for analyzing spatially resolved properties of galaxies using imaging data or a combination of imaging data and the integral field spectroscopy (IFS) data. piXedfit has six modules that can handle all tasks in the analysis of the spatially resolved SEDs of galaxies, including images processing, a spatial-matching between reduced broad-band images with an IFS data cube, pixel binning, performing SED fitting, and making visualization plots for the SED fitting results.

[ascl:2207.034]
SSHT: Fast spin spherical harmonic transforms

SSHT performs fast and exact spin spherical harmonic transforms; functionality is also provided to perform fast and exact adjoint transforms, forward and inverse transforms, and spherical harmonic transforms for a number of alternative sampling schemes. The code can interface with DUCC (ascl:2008.023) and use it as a backend for spherical harmonic transforms and rotations.

[ascl:2207.035]
massmappy: Mapping dark matter on the celestial sphere

massmappy recovers convergence mass maps on the celestial sphere from weak lensing cosmic shear observations. It relies on SSHT (ascl:2207.034) and HEALPix (ascl:1107.018) to handle sampled data on the sphere. The spherical Kaiser-Squires estimator is implemented.

[submitted]
BMarXiv

BMarXiv scans new (i.e., since the last time checked) submissions from arXiv, ranks submissions based on keyword matches, and produces an HTML page as an output.

The keywords are looked for (with regex capabilities) in the title, abstract, but also the author list, so it is possible to look for people too. The score is calculated for each specific entry but additional (and optional) scoring is performed using the first author recent submissions and/or the other authors' recent submissions.

It is possible to include/exclude any arXiv categories (within astro-ph or not). New astronomical conferences (from CADC by default) and new codes (from ASCL.net) are also checked and can also be scanned for keywords.

A local bibliography file can be scanned to find frequent words/groups of words that could become scanned keywords.

[submitted]
Eidein: Interactive Visualization Tool for Deep Active Learning

Eidein interactively visualizes a data sample for the selection of an informative (contains data with high predictive uncertainty, is diverse, but not redundant) data subsample for deep active learning. The data sample is projected to 2-D with a dimensionality reduction technique. It is visualized in an interactive scatter plot that allows a human expert to select and annotate the data subsample.

[ascl:2208.001]
BlaST: Synchrotron peak estimator for blazars

BlaST (Blazar Synchrotron Tool) estimates the synchrotron peak of blazars given their spectral energy distribution. It uses a machine-learning algorithm that simplifies the estimation and also provides a reliable uncertainty estimation. The package naturally accounts for additional SED components from the host galaxy and the disk emission. BlaST also supports bulk estimation, *e.g.* estimating a whole catalog, by providing a directory or zip file containing the seds as well as an output file in which to write the results.

[ascl:2208.002]
qrpca: QR-based Principal Components Analysis

qrpca uses QR-decomposition for fast principal component analysis. The software is particularly suited for large dimensional matrices. It makes use of torch for internal matrix computations and enables GPU acceleration, when available. Written in both R and python languages, qrpca provides functionalities similar to the prcomp (R) and sklearn (python) packages.

[ascl:2208.003]
Scatfit: Scattering fits of time domain radio signals (Fast Radio Bursts or pulsars)

Scatfit models observed burst signals of impulsive time domain radio signals (* e.g.*, Fast Radio Bursts (FRBs) or pulsars pulses), which usually are convolution products of various effects, and fits them to the experimental data. It includes several models for scattering and instrumental effects. The code loads the experimental time domain radio data, cleans them, fits an aggregate scattering model to the data, and robustly estimates the model parameters and their uncertainties. Additionally, scatfit determines the scaling of the scattering time with frequency, *i.e.* the scattering index, and the scattering-corrected dispersion measure of the burst or pulse.

[ascl:2208.004]
TOM Toolkit: Target and Observation Manager Toolkit

Lindstrom, William; Chatelain, Joseph; Collom, David; Riba, Austin; Street, Rachel; McCully, Curtis; Bowman, Mark

The TOM Toolkit combines a flexible, searchable database of all information related to a scientific research project, with an observation and data analysis control system, and communication and data visualization tools. This Toolkit includes a fully operational TOM (Target and Observation Manager) system in addition to a range of optional tools for specific tasks, including interfaces to widely-used observing facilities and data archives and data visualization tools. With TOM Toolkit, project teams can develop and customize a system for their own science goals, without needing specialist expertise in databasing.

[ascl:2208.005]
Asymmetric Uncertainty: Handling nonstandard numerical uncertainties

Asymmetric Uncertainty implements and provides an object class for dealing with uncertainties for physical quantities that are not symmetric. Instances of the class behave appropriately with other numeric objects under most mathematical operations, and the associated errors propagate accordingly. The class also provides utilities such as methods for evaluating and plotting probability density functions, as well as capabilities for handling arrays of such objects. Standard and symmetric uncertainties are also supported.

[ascl:2208.006]
ThermoEngine: Thermodynamic properties estimator and phase equilibrium calculator

ThermoEngine estimates the thermodynamic properties of minerals, fluids, and melts, and calculates phase equilibriums. The Equilibrate module of ThermoEngine provides Python functions and classes for computing equilibrium phase assemblages with focus on MELTS calculations. The Phases module includes Python functions and classes for computing standard thermodynamic calculations utilizing the Berman, Holland and Powell, or Stixrude-Lithgow-Bertelloni endmember databases, and calculations based on solution properties utilized by MELTS. There are many helper functions available in this module that assist in the calculation of pseudosections, univariant equilibria and the construction of phase diagrams.

[ascl:2208.007]
VapoRock: Modeling magma ocean atmospheres and stellar nebula

VapoRock calculates the equilibrium partial pressures of metal-bearing gas species of specific elements above the magma ocean surface to determine the metal-bearing composition of the atmosphere as a function of temperature and the bulk composition of the magma ocean. It utilizes ENKI's ThermoEngine (ascl:2208.006) and combines estimates for element activities in silicate melts with thermodynamic data for metal and metal oxide vapor species.

[ascl:2208.008]
RJ-plots: Automated objective classification of 2D structures

RJ-plots uses a moments of inertia method to disentangle a 2D structure's elongation from its centrally over/under-density, thus providing a means for the automated and objective classification of such structures. It may be applied to any 2D pixelated image such as column density maps or moment zero maps of molecular lines. This method is a further development of J-plots (ascl:2009.007).

[ascl:2208.009]
LeXInt: Leja Exponential Integrators

LeXInt (Leja interpolation for eXponential Integrators) is a temporal exponential integration package using the method of polynomial interpolation at Leja points. Exponential Rosenbrock (EXPRB) and Exponential Propagation Iterative Runge-Kutta (EPIRK) methods use the Leja interpolation method to compute the functions. For linear PDEs, one can get the exact solution (in time) by directly computing the matrix exponential.

[ascl:2208.010]
FFD: Flare Frequency Distribution

FFD (Flare Frequency Distribution) fits power-laws to FFDs. FFDs relate the frequency (*i.e.*, occurrence rate) of flares to their energy, peak flux, photometric equivalent width, or other parameters. This module was created to handle disparate datasets between which the flare detection limit varies; in essence, the number of flares detected is treated as following a Poisson distribution while the flare energies are treated as following a power law.

[ascl:2208.011]
POIS: Python Optical Interferometry Simulation

POIS (Python Optical Interferometry Simulation) provides the building blocks to simulate the operation of a ground-based optical interferometer perturbed by atmospheric seeing perturbations. The package includes functions to generate simulated atmospheric turbulent wavefront perturbations, correct these perturbations using adaptive optics, and combine beams from an arbitrary number of telescopes, with or without spatial filtering, to provide complex fringe visibility measurements.

[ascl:2208.012]
DELIGHT: Identify host galaxies of transient candidates

Förster, Francisco; Muñoz Arancibia, Alejandra M.; Reyes, Ignacio; Gagliano, Alexander; Britt, Dylan; Cuellar-Carrillo, Sara; Figueroa-Tapia, Felipe; Polzin, Ava; Yousef, Yara; Arredondo, Javier; Rodríguez-Mancini, Diego; Correa-Orellana, Javier; Bayo, Amelia; Bauer, Franz E.; Catelan, Márcio; Cabrera-Vives, Guillermo; Dastidar, Raya; Estévez, Pablo A.; Pignata, Giuliano; Hernandez-Garcia, Lorena; Huijse, Pablo; Reyes, Esteban; Sánchez-Sáez, Paula; Ramirez, Mauricio; Grandón, Daniela; Pineda-García, Jonathan; Chabour-Barra, Francisca; Silva-Farfán, Javier

DELIGHT (Deep Learning Identification of Galaxy Hosts of Transients) automatically identifies host galaxies of transient candidates using multi-resolution images and a convolutional neural network. This library has a class with several methods to get the most likely host coordinates starting from given transient coordinates. In order to do this, the DELIGHT object needs a list of object identifiers and coordinates (oid, ra, dec). With this information, it downloads PanSTARRS images centered around the position of the transients (2 arcmin x 2 arcmin), gets their WCS solutions, creates the multi-resolution images, does some extra preprocessing of the data, and finally predicts the position of the hosts using a multi-resolution image and a convolutional neural network. DELIGHT can also estimate the host's semi-major axis if requested, taking advantage of the multi-resolution images.

[ascl:2208.013]
SPAMMS: Spectroscopic PAtch Model for Massive Stars

Abdul-Masih, Michael; Sana, Hugues; Conroy, Kyle E.; Sundqvist, Jon; Prša, Andrej; Kochoska, Angela; Puls, Joachim

SPAMMS (Spectroscopic PAtch Model for Massive Stars), designed with geometrically deformed systems in mind, combines the eclipsing binary modelling code PHOEBE 2 (ascl:1106.002) and the NLTE radiative transfer code FASTWIND to produce synthetic spectra for systems at given phases, orientations and geometries. SPAMMS reproduces the morphology of observed spectral line profiles for overcontact systems and the Rossiter-Mclaughlin and Struve-Sahade effects.

[ascl:2208.014]
uvcombine: Combine images with different resolutions

uvcombine combines single-dish and interferometric data. It can combine high-resolution images that are missing large angular scales (Fourier-domain short-spacings) with low-resolution images containing the short/zero spacing. uvcombine includes the "feathering" technique for interferometry data, implementing a similar approach to CASA’s (ascl:1107.013) feather task but with additional options. Also included are consistency tests for the flux calibration and single-dish scale by comparing the data in the uv-overlap range.

[ascl:2208.015]
J-comb: Combine high-resolution and low-resolution data

J-comb combines high-resolution data with large-scale missing information with low-resolution data containing the short spacing. Based on uvcombine (ascl:2208.014), it takes as input FITS files of low- and high-resolution images, the angular resolution of the input images, and the pixel size of the input images, and outputs a FITS file of the combined image.

[ascl:2208.016]
CRPropa3: Simulation framework for propagating extraterrestrial ultra-high energy particles

Alves Batista, Rafael; Dundovic, Andrej; Erdmann, Martin; Kampert, Karl-Heinz; Kuempel, Daniel; Müller, Gero; Sigl, Guenter; van Vliet, Arjen; Walz, David; Winchen, Tobias

CRPropa3, an improved version of CRPropa2 (ascl:1412.013), provides a simulation framework to study the propagation of ultra-high-energy nuclei up to iron on their voyage through an (extra)galactic environment. It takes into account pion production, photodisintegration, and energy losses by pair production of all relevant isotopes in the ambient low-energy photon fields, as well as nuclear decay. CRPropa3 can model the deflection in (inter)galactic magnetic fields, the propagation of secondary electromagnetic cascades, and neutrinos for a multitude of scenarios for different source distributions and magnetic environments. It enables the user to predict the spectra of UHECR (and of their secondaries), their composition and arrival direction distribution. Additionally, the low-energy Galactic propagation can be simulated by solving the transport equation using stochastic differential equations. CRPropa3 features a very flexible simulation setup with python steering and shared-memory parallelization.

[ascl:2208.017]
HOCHUNK3D: Dust radiative transfer in 3D

HOCHUNK3D is an updated version of the HOCHUNK radiative equilibrium code (ascl:1711.013); the code has been converted to Fortran 95, which allows a specification of one-dimensional (1D), 2D, or 3D grids at runtime. The code is parallelized so it can be run on multiple processors on one machine, or on multiple machines in a network. It includes 3-D functionality and several other additional geometries and features. The code calculates radiative equilibrium temperature solution, thermal and PAH/vsg emission, scattering and polarization in protostellar geometries. HOCHUNK3D also computes spectral energy distributions (SEDs), polarization spectra, and images.

[ascl:2208.018]
EstrellaNueva: Expected rates of supernova neutrinos calculator

EstrellaNueva calculates expected rates of supernova neutrinos in detectors. It provides a link between supernova simulations and the expected events in detectors by calculating fluences and event rates in order to ease any comparison between theory and observation. The software is a standalone tool for exploring many physics scenarios, and offers an option to add analytical cross sections and define any target material.

[ascl:2208.019]
RadioLensfit: Radio weak lensing shear measurement in the visibility domain

RadioLensfit measures star-forming galaxy ellipticities using a Bayesian model fitting approach. The software uses an analytical exponential Sersic model and works in the visibility domain avoiding Fourier Transform. It also simulates visibilities of observed SF galaxies given a source catalog and Measurement Sets containing the description of the radio interferometer and of the observation. It provides both serial and MPI versions.

[ascl:2208.020]
GStokes: Magnetic field structure and line profiles calculator

GStokes performs simple multipolar fits to circular polarization data to provide information about the field strength and geometry. It provides forward calculation of the disc-integrated Stokes parameter profiles as well as magnetic inversions under several widely used simplifying approximations of the polarized line formation. GStokes implements the Unno–Rachkovsky analytical solution of the polarized radiative transfer equation and the weak-field approximation with the Gaussian local profiles. The magnetic field geometry is described with one of the common low-order multipolar field parametrizations. Written in IDL, GStokes provides a user-friendly graphical front-end.

[ascl:2208.021]
GSSP: Grid Search in Stellar Parameters

GSSP (Grid Search in Stellar Parameters) is based on a grid search in the fundamental atmospheric parameters and (optionally) individual chemical abundances of the star (or binary stellar components) in question. It uses atmosphere models and spectrum synthesis, which assumes a comparison of the observations with each theoretical spectrum from the grid. The code can optimize five stellar parameters at a time (effective temperature, surface gravity, metallicity, microturbulent velocity, and projected rotational velocity of the star) and synthetic spectra can be computed in any number of wavelength ranges. GSSP builds the grid of theoretical spectra from all possible combinations of the above mentioned parameters, and delivers the set of best fit parameters, the corresponding synthetic spectrum, and the ASCII file containing the individual parameter values for all grid points and the corresponding chi-square values.

[ascl:2208.022]
PyNAPLE: Automated pipeline for detecting changes on the lunar surface

PyNAPLE (PYthon Nac Automated Pair Lunar Evaluator) detects changes and new impact craters on the lunar surface using Lunar Reconnaissance Orbiter Narrow Angle Camera (LRO NAC) images. The code enables large scale analyses of sub-kilometer scale cratering rates and refinement of both scaling laws and the luminous efficiency.

[ascl:2208.023]
CubeFit: Regularized 3D fitting for spectro-imaging data

Cubefit is an OXY class that performs spectral fitting with spatial regularization in a spectro-imaging context. The 3D model is based on a 1D model and 2D parameter maps; the 2D maps are regularized using an L1L2 regularization by default. The estimator is a compound of a chi^2 based on the 1D model, a regularization term based of the 2D regularization of the various 2D parameter maps, and an optional decorrelation term based on the cross-correlation of specific pairs of parameter maps.

[ascl:2208.024]
toise: Performance estimator for high-energy neutrino detectors

The toise framework estimates the sensitivity of natural-medium neutrino detectors such as IceCube-Gen2 to sources of high-energy astrophysical neutrinos. It uses parameterizations of a detector's fiducial area or volume, selection efficiency, energy resolution, angular resolution, and event classification efficiency to convert (surface) neutrino fluxes into mean event rates in bins of observable space. These are then used to estimate statistical quantities of interest, *e.g.*, the median sensitivity to some flux (*i.e.*, 90% upper limit assuming the true flux is zero) or the median discovery potential (*i.e.*, the flux level at which the null hypothesis would be rejected at 5 sigma in 50% of realizations).

[ascl:2208.025]
Yonder: Data denoising and reconstruction

YONDER uses singular value decomposition to perform low-rank data denoising and reconstruction. It takes a tabular data matrix and an error matrix as input and returns a denoised version of the original dataset as output. The approach enables a more accurate data analysis in the presence of uncertainties. Consequently, this package can be used as a simple toolbox to perform astronomical data cleaning.

[ascl:2209.001]
A-SLOTH: Semi-analytical model to connect first stars and galaxies to observables

A-SLOTH (Ancient Stars and Local Observables by Tracing Halos) connects the formation of the first stars and galaxies to observables. The model is based on dark matter merger trees, on which A-SLOTH applies analytical recipes for baryonic physics to model the formation of both metal-free and metal-poor stars and the transition between them. The software samples individual stars and includes radiative, chemical, and mechanical feedback. A-SLOTH has versatile applications with moderate computational requirements. It can be used to constrain the properties of the first stars and high-z galaxies based on local observables, predicts properties of the oldest and most metal-poor stars in the Milky Way, can serve as a subgrid model for larger cosmological simulations, and predicts next-generation observables of the early Universe, such as supernova rates or gravitational wave events.

[ascl:2209.002]
Herculens: Differentiable gravitational lensing

Herculens models imaging data of strong gravitational lenses. The package supports various degrees of model complexity, ranging from standard smooth analytical profiles to pixelated models and machine learning approaches. In particular, it implements multiscale pixelated models regularized with sparsity constraints and wavelet decomposition, for modeling both the source light distribution and the lens potential. The code is fully differentiable - based on JAX (ascl:2111.002) - which enables fast convergence to the solution, access to the parameters covariance matrix, efficient exploration of the parameter space including the sampling of posterior distributions using variational inference or Hamiltonian Monte-Carlo methods.

[ascl:2209.003]
DeepMass: Cosmological map inference with deep learning

DeepMass infers dark matter maps from weak gravitational lensing measurements and uses deep learning to reconstruct cosmological maps. The code can also be incorporated into a Moment Network to enable high-dimensional likelihood-free inference.

[ascl:2209.004]
Cluster Toolkit: Tools for analyzing galaxy clusters

Cluster Toolkit calculates weak lensing signals from galaxy clusters and cluster cosmology. It offers 3D density and correlation functions, halo bias models, projected density and differential profiles, and radially averaged profiles. It also calculates halo mass functions, mass-concentration relations, Sunyaev-Zel’dovich (SZ) cluster signals, and cluster magnification. Cluster Toolkit consists of a Python front end wrapped around a well optimized back end in C.

[ascl:2209.005]
SCORE: Shape COnstraint REstoration

The Shape COnstraint REstoration algorithm (SCORE) is a proximal algorithm based on sparsity and shape constraints to restore images. Its main purpose is to restore images while preserving their shape information. It can, for example, denoise a galaxy image by instanciating SCORE and using its denoise method and then visualize the results, and can deconvolve multiple images with different parameter values.

[ascl:2209.006]
KaRMMa: Curved-sky mass map reconstruction

KaRMMa (Kappa Reconstruction for Mass MApping) performs curved-sky mass map reconstruction using a lognormal prior from weak-lensing surveys. It uses a fully Bayesian approach with a physically motivated lognormal prior to sample from the posterior distribution of convergence maps. The posterior distribution of KaRMMa maps are nearly unbiased in one-point and two-point functions and peak/void counts. KaRMMa successfully captures the non-Gaussian nature of the distribution of κ values in the simulated maps, and KaRMMa posteriors correctly characterize the uncertainty in summary statistics.

[ascl:2209.007]
AMBER: Fast pipeline for detecting single-pulse radio transients

AMBER (Apertif Monitor for Bursts Encountered in Real-time) detects single-pulse radio phenomena, such as pulsars and fast radio bursts, in real time. It is a fully auto-tuned pipeline that offloads compute-intensive kernels to many-core accelerators; the software automatically tunes these kernels to achieve high performance on different platforms.

[ascl:2209.008]
PINION: Accelerating radiative transfer simulations for cosmic reionization

PINION (Physics-Informed neural Network for reIONization) predicts the complete 4-D hydrogen fraction evolution from the smoothed gas and mass density fields from pre-computed N-body simulations. Trained on C^{2}-Ray simulation outputs with a physics constraint on the reionization chemistry equation, PINION accurately predicts the entire reionization history between z = 6 and 12 with only five redshift snapshots and a propagation mask as a simplistic approximation of the ionizing photon mean free path. The network's predictions are in good agreement with simulation to redshift z > 7, though the oversimplified propagation mask degrades the network's accuracy for z < 7.

[ascl:2209.009]
GRUMPY: Galaxy formation with RegUlator Model in PYthon

GRUMPY (Galaxy formation with RegUlator Model in PYthon) models the formation of dwarf galaxies. When coupled with realistic mass accretion histories of halos from simulations and reasonable choices for model parameter values, this simple regulator-type framework reproduces a broad range of observed properties of dwarf galaxies over seven orders of magnitude in stellar mass. GRUMPY matches observational constraints on the stellar mass--halo mass relation and observed relations between stellar mass and gas phase and stellar metallicities, gas mass, size, and star formation rate. It also models the general form and diversity of star formation histories (SFHs) of observed dwarf galaxies. The software can be used to predict photometric properties of dwarf galaxies hosted by dark matter haloes in N-body simulations, such as colors, surface brightnesses, and mass-to-light ratios and to forward model observations of dwarf galaxies.

[ascl:2209.010]
HyPhy: Hydrodynamical Physics via Deep Generative Painting

HyPhy maps from dark matter only simulations to full hydrodynamical physics models. It uses a fully convolutional variational auto-encoder (VAE) to synthesize hydrodynamic fields conditioned on dark matter fields from N-body simulations. After training, HyPhy can probabilistically map new dark matter only simulations to corresponding full hydrodynamical outputs and generate posterior samples for studying the variance of the mapping. This conditional deep generative model is implemented in TensorFlow.

[ascl:2209.011]
GaLight: 2D modeling of galaxy images

Ding, Xuheng; Silverman, John; Birrer, Simon; Treu, Tommaso; Tang, Shenli; Yang, Lilan; Bottrell, Connor

GaLight (Galaxy shapes of Light) performs two-dimensional model fitting of optical and near-infrared images to characterize the light distribution of galaxies with components including a disk, bulge, bar and quasar. Light is decomposes into PSF and Sersic, and the fitting is based on lenstronomy (ascl:1804.01). GaLight's automated features including searching PSF stars in the FOV, automatically estimating the background noise level, and cutting out the target object galaxies (QSOs) and preparing the materials to model the data. It can also detect objects in the cutout stamp and quickly create Sersic keywords to model them, and model QSOs and galaxies using 2D Sersic profile and scaled point source.

[ascl:2209.012]
URILIGHT: Time-dependent Monte-Carlo radiative-transfer

The time dependent Monte-Carlo code URILIGHT, written in Fortran 90, assumes homologous expansion. Energy deposition resulting from the decay of radioactive isotopes is calculated by a Monte-Carlo solution of the γ-ray transport, for which interaction with matter is included through Compton scattering and photoelectric absorption. The temperature is iteratively solved for in each cell by requiring that the total emissivity equals the total absorbed energy.

[ascl:2209.013]
wsynphot: Synthetic photometry package using quantities

wsynphot provides a broad set of filters, including observation facility, instrument, and wavelength range, and functions for imaging stars to produce a filter curve showing the transmission of light for each wavelength value. It can create a filter curve object, plot the curve, and allows the user to do calculations on the filter curve object.

[ascl:2209.014]
SyntheticISOs: Synthetic Population of Interstellar Objects

Synthetic Population of Interstellar Objects generates a synthetic population of interstellar objects (orbits and sizes) in arbitrary volume of space around the Sun. The only necessary assumption is that the population of ISOs in the interstellar space (far from any massive body) is homogeneous and isotropic. The assumed distribution of interstellar velocities of ISOs has to be provided as an input. This distribution can be defined analytically, but also in a discrete form. The algorithm, based on the multivariate inverse transform sampling method, is implemented in Python.

[ascl:2209.015]
TauREx3: Tau Retrieval for Exoplanets

TauREx 3 (Tau Retrieval for Exoplanets) provides a fully Bayesian inverse atmospheric retrieval framework for exoplanetary atmosphere modeling and retrievals. It is fully customizable, allowing the user to mix and match atmospheric parameters and add additional ones. The framework builds forward models, simulates instruments, and performs retrievals, and provides a rich library of classes for building additional programs and using new atmospheric parameters.

[ascl:2209.016]
RAPOC: Rosseland and Planck mean opacities calculator

RAPOC (Rosseland and Planck Opacity Converter) uses molecular absorption measurements (*i.e.*, wavelength-dependent opacities) for a given temperature, pressure, and wavelength range to calculate Rosseland and Planck mean opacities for use in atmospheric modeling. The code interpolates between discrete data points and can use ExoMol and DACE data, or any user-defined data provided in a readable format. RAPOC is simple, straightforward, and easily incorporated into other codes.

[ascl:2209.017]
SpectraPy: Extract and reduce astronomical spectral data

SpectraPy collects algorithms and methods for data reduction of astronomical spectra obtained by a through slits spectrograph. It produces two-dimensional wavelength calibrated spectra corrected by instrument distortions. The library is designed to be spectrograph independent and can be used on both longslit (LS) and multi object spectrograph (MOS) data. SpectraPy comes with a set of already configured spectrographs, but it can be easily configured to reduce data of other instruments.

[ascl:2209.018]
libTheSky: Compute positions of celestial bodies and events

libTheSky compute the positions of celestial bodies, such as the Moon, planets, and stars, and events, including conjunctions and eclipses, with great accuracy. Written in Fortran, libTheSky can use different reference frames (heliocentric, geocentric, topocentric) and coordinate systems (ecliptic, equatorial, galactic; spherical, rectangular), and the user can choose low- or high-accuracy calculations, depending on need.

[ascl:2209.019]
SolTrack: Compute the position of the Sun in topocentric coordinates

SolTrack computes the position of the Sun, the rise and set times and azimuths, and transit times and altitudes. It includes corrections for aberration and parallax, and has a simple routine to correct for atmospheric refraction, taking into account local atmospheric conditions. SolTrack is derived from the Fortran library libTheSky (ascl:2209.018). The package can be used to track the Sun on a low-specs machine, such as a microcontroller or PLC, and can be used for (highly) concentrated (photovoltaic) solar power or accurate solar-energy modeling.

[ascl:2209.020]
FastQSL: Quasi-separatrix Layers computation method

FastQSL calculate the squashing factor Q at the photosphere, a cross section, or a box volume, given a 3D magnetic field with Cartesian, uniform or stretched grids. It is available in IDL and in an optimized version using Fortran for calculations and field line tracing. Use of a GPU accelerates a step-size adaptive scheme for the most computationally intensive part, the field line tracing, making the code fast and efficient.

[submitted]
EleFits

EleFits is a modern C++ package to read and write FITS files which focuses on safety, user-friendliness, and performance.

[ascl:2210.001]
PSS: Pulsar Survey Scraper

Pulsar Survey Scraper aggregates pulsar discoveries before they are included in the ATNF pulsar catalog and enables searching and filtering based on position and dispersion measure. This facilitates identifying new pulsar discoveries. Pulsar Survey Scraper can be downloaded or run online using the Pulsar Survey Scraper webform.

[ascl:2210.002]
SPINspiral: Parameter estimation for analyzing gravitational-wave signals

SPINspiral analyzes gravitational-wave signals from stellar-mass binary inspirals detected by ground-based interferometers such as LIGO and Virgo. It performs parameter estimation on these signals using Markov-chain Monte-Carlo (MCMC) techniques. This analysis includes the spins of the binary components. Written in C, the package is modular; its main routine is as small as possible and calls other routines, which perform tasks such as reading input, choosing and setting (starting or injection) parameters, and handling noise. Other routines compute overlaps and likelihoods, contain the MCMC core, and manage more general support functions and third-party routines.

[ascl:2210.003]
NIRDust: Near Infrared Dust finder for Type2 AGN K-band spectra

NIRDust uses K-band (2.2 micrometers) spectra to measure the temperature of the dust heated by an Active Galactic Nuclei (AGN) accretion disk. The package provides several functionalities to pre-process spectra and fit the hot dust component of a AGN K-band spectrum with a blackbody function. NIRDust needs a minimum of two spectra to run: a target spectrum, where the dust temperature will be estimated, and a reference spectrum, where the emission is considered to be purely stellar. The reference spectrum will be used by NIRDust to model the stellar emission from the target spectrum.

[ascl:2210.004]
Finder_charts: Create finder charts from image data of various sky surveys

Finder_charts creates multi-band finder charts from image data of various partial- and all-sky surveys such as DSS, 2MASS, WISE, UKIDSS, VHS, Pan-STARRS, and DES. It also creates a WISE time series of image data acquired between 2010 and 2021. All images are reprojected so that north is up and east is to the left. The resulting finder charts can be overplotted with corresponding catalog positions. All catalog entries within the specified field of view can be saved in a variety of formats, including ipac, csv, and tex, as can the finder charts in png, pdf, eps, and other common graphics formats. Finder_charts consists of a single Python module, which depends only on well-known packages, making it easy to install.

[ascl:2210.005]
PSFr: Point Spread Function reconstruction

PSFr empirically reconstructs an oversampled version of the point spread function (PSF) from astronomical imaging observations. The code provides a light-weighted API of a refined version of an algorithm originally implemented in lenstronomy (ascl:1804.012). It provides user support with different artifacts in the data and supports the masking of pixels, or the treatment of saturation levels. PSFr has been used to reconstruct the PSF from multiply imaged lensed quasar images observed by the Hubble Space Telescope in a crowded lensing environment and more recently with James Webb Space Telescope (JWST) imaging data for a wide dynamical flux range.

[ascl:2210.006]
ExoRad2: Generic point source radiometric model

ExoRad 2.0, a generic point source radiometric model, interfaces with any instrument to provide an estimate of several Payload performance metrics. For each target and for each photometric and spectroscopic channel, the code provides estimates of signals in pixels, saturation times, and read, photon, and dark current noise. ExoRad also provides estimates for the zodiacal background, inner sanctum, and sky foreground.

[ascl:2210.007]
COMET: Emulated predictions of large-scale structure observables

Eggemeier, Alexander; Camacho-Quevedo, Benjamin; Pezzotta, Andrea; Crocce, Martin; Scoccimarro, Román; Sánchez, Ariel G.

COMET (Clustering Observables Modelled by Emulated perturbation Theory) provides emulated predictions of large-scale structure observables from models that are based on perturbation theory. It substantially speeds up these analytic computations without any relevant sacrifice in accuracy, enabling an extremely efficient exploration of large-scale structure likelihoods. At its core, COMET exploits an evolution mapping approach which gives it a high degree of flexibility and allows it to cover a wide cosmology parameter space at continuous redshifts up to z∼3z \sim 3z∼3. Among others, COMET supports parameters for cold dark matter density (ωc\omega_cωc), baryon density (ωb\omega_bωb), Scalar spectral index (nsn_sns), Hubble expansion rate (hhh) and Curvature density (ΩK\Omega_KΩK). The code can obtain the real-space galaxy power spectrum at one-loop order multipoles (monopole, quadrupole, hexadecapole) of the redshift-space, power spectrum at one-loop order, the linear matter power spectrum (with and without infrared resummation), Gaussian covariance matrices for the real-space power spectrum, and redshift-space multipoles and χ2\chi^2χ2's for arbitrary combinations of multipoles. COMET provides an easy-to-use interface for all of these computations.

[ascl:2210.008]
RADTRAN: General purpose planetary radiative transfer model

RADTRAN calculates the transmission, absorption or emission spectra emitted by planetary atmospheres using either line-by-line integration, spectral band models, or 'correlated-K' approaches. Part of the NEMESIS project (ascl:2210.009), the code also incorporates both multiple scattering and single scattering calculations. RADTRAN is general purpose and not hard-wired to any specific planet.

[ascl:2210.009]
NEMESIS: Non-linear optimal estimator for multivariate spectral analysis

Irwin, P. G. J.; Teanby, N. A.; de Kok, R.; Fletcher, L. N.; Howett, C. J. A.; Tsang, C. C. C.; Wilson, C. F.; Calcutt, S. B.; Nixon, C. A.; Parrish, P. D.

NEMESIS (Non-linear optimal Estimator for MultivariatE spectral analySIS) is the general purpose correlated-k/LBL retrieval code developed from the RADTRAN project (ascl:2210.008). Originally based on the correlated-k approximation, NEMESIS also works in line-by-line (LBL) mode. It has been designed to be generally applicable to any planet and with any observing mode and so is suitable for both solar-system studies and also exoplanetary studies.

[ascl:2210.010]
TSRecon: Time series reconstruction method of massive astronomical catalogs

The time series reconstruction method of massive astronomical catalogs reconstructs all celestial objects' time series data for astronomical catalogs with great accuracy. In addition, the program, which requires a Spark cluster, solves the boundary source leakage problem on the premise of ensuring accuracy, and the user can set different parameters for different data sets to filter the error records in the catalogs.

[ascl:2210.011]
gbdes: DECam instrumental signature fitting and processing programs

gbdes derives photometric and astrometric calibration solutions for complex multi-detector astronomical imagers. The package includes routines to filter catalogs down to useful stellar objects, collect metadata from the catalogs and create a config file holding FITS binary tables describing exposures, instruments, fields, and other available information in the data, and uses a friends-of-friends matching algorithm to link together all detections of common objects found in distinct exposures. gbdes also calculates airmasses and parallactic angles for each exposure, calculates and saves the expected differential chromatic refraction (DCR) needed for precision astrometry, optimizes the parameters of a photometric model to maximize agreement between magnitudes measured in different exposures of the same source, and optimizing the parameters of an astrometric model to maximize agreement among the exposures and any reference catalogs, and performs other tasks. The solutions derived and used by gbdes are stored in YAML format; gbdes uses the Python code pixmappy (ascl:2210.012) to read the astrometric solution files and execute specified transformations.

[ascl:2210.012]
pixmappy: Python interface to gbdes astrometry solutions

pixmappy provides a Python interface to gbdes pixel map (astrometry) solutions. It reads the YAML format astrometry solutions produced by gbdes (ascl:2210.011) and issues a PixelMap instance, which is a map from one 2d coordinate system ("pixel") to another ("world") 2d system. A PixelMap instance can be used as a function mapping one (or many) coordinate pairs. An inverse method does reverse mapping, and the local jacobian of the map is available also. The type of mapping that can be expressed is very flexible, and PixelMaps can be compounded into chains of tranformations.

[ascl:2210.013]
iharm3D: Hybrid MPI/OpenMP 3D HARM with vectorization

Prather, Ben; Wong, George; Dhruv, Vedant; Ryan, Benjamin; Dolence, Joshua; Ressler, Sean; Gammie, Charles

iharm3D implements the HARM algorithm (ascl:1209.005) with modifications and enables a second-order, conservative, shock-capturing scheme for general-relativistic magnetohydrodynamics (GRMHD). Written in C, it simulates black hole accretion systems in arbitrary stationary spacetimes.

[ascl:2210.014]
Blacklight: GR ray tracing code for post-processing Athena++ simulations

Blacklight postprocesses general-relativistic magnetohydrodynamic simulation data and produces outputs for analyzing data sets, including maps of auxiliary quantities and false-color renderings. The code can use Athena++ (ascl:1912.005) outputs directly, and also supports files in HARM (ascl:1209.005) and iHARM3d (ascl:2210.013) format. Written in C++, Blacklight offers support for adaptive mesh refinement input, slow-light calculations, and adaptive ray tracing.

[ascl:2210.015]
Solar-MACH: Multi-spacecraft longitudinal configuration plotter

Gieseler, Jan; Dresing, Nina; Palmroos, Christian; von Forstner, Johan L. Freiherr; Price, Daniel J.; Vainio, Rami; Kouloumvakos, Athanasios; Rodríguez-García, Laura; Trotta, Domenico; Génot, Vincent; Masson, Arnaud; Roth, Markus; Veronig, Astrid

Solar-MACH (Solar MAgnetic Connection HAUS) derives and visualizes the spatial configuration and solar magnetic connection of different observers (*i.e.*, spacecraft or planets) in the heliosphere at different times. It provides publication-ready figures for analyzing Solar Energetic Particle events (SEPs) or solar transients such as Coronal Mass Ejections (CMEs). Solar-MACH is available as a Python package; a Streamlit-enabled tool that runs in a browser is also available (solar-mach.github.io)

[ascl:2210.016]
PETSc: Portable, Extensible Toolkit for Scientific Computation

Balay, Satish; Abhyankar, Shrirang; Adams, Mark F.; Benson, Steven; Brown, Jed; Brune, Peter; Buschelman, Kris; Constantinescu, Emil; Dalcin, Lisandro; Dener, Alp; Eijkhout, Victor; Faibussowitsch, Jacob; Gropp, William D.; Hapla, Vaclav; Isaac, Tobin; Jolivet, Pierre; Karpeev, Dmitry; Kaushik, Dinesh; Knepley, Matthew G.; Kong, Fande; Kruger, Scott; May, Dave A.; McInnes, Lois Curfman; Mills, Richard Tran; Mitchell, Lawrence; Munson, Todd; Roman, Jose E.; Rupp, Karl; Sanan, Patrick; Sarich, Jason; Smith, Barry F.; Zampini, Stefano; Zhang, Hong; Zhang, Junchao

PETSc (Portable, Extensible Toolkit for Scientific Computation) provides a suite of data structures and routines for the scalable (parallel) solution of scientific applications modeled by partial differential equations, and is intended for use in large-scale application projects. The toolkit includes a large suite of parallel linear, nonlinear equation solvers and ODE integrators that are easily used in application codes written in C, C++, Fortran and Python. PETSc provides many of the mechanisms needed within parallel application codes, such as simple parallel matrix and vector assembly routines that allow the overlap of communication and computation. In addition, PETSc (pronounced PET-see) includes support for managing parallel PDE discretizations.

[ascl:2210.017]
PySME: Spectroscopy Made Easy reimplemented with Python

PySME is a partial reimplementation of Spectroscopy Made Easy (SME, ascl:1202.013), which fits an observed spectrum of a star with a model spectrum. The IDL routines of SME used to call a dynamically linked library of compiled C++ and Fortran programs have been rewritten in Python. In addition, an object oriented paradigm and continuous integration practices, including build automation, self-testing, and frequent builds, have been added.

[ascl:2210.018]
LavAtmos: Gas-melt equilibrium calculations for a given temperature and melt composition

LavAtmos performs gas-melt equilibrium calculations for a given temperature and melt composition. The thermodynamics of the melt are modeled by the MELTS code as presented in the Thermoengine package (ascl:2208.006). In combination with atmospheric chemistry codes, LavAtmos enables the characterization of interior compositions through atmospheric signatures.

[ascl:2210.019]
POSYDON: Single and binary star population synthesis code

Fragos, Tassos; Andrews, Jeff J.; Bavera, Simone S.; Berry, Christopher P. L.; Coughlin, Scott; Dotter, Aaron; Giri, Prabin; Kalogera, Vicky; Katsaggelos, Aggelos; Kovlakas, Konstantinos; Lalvani, Shamal; Misra, Devina; Srivastava, Philipp M.; Qin, Ying; Rocha, Kyle A.; Roman-Garza, Jaime; Serra, Juan Gabriel; Stahle, Petter; Sun, Meng; Teng, Xu; Trajcevski, Goce; Hai Tran, Nam; Xing, Zepei; Zapartas, Emmanouil; Zevin, Michael

POSYDON (POpulation SYnthesis with Detailed binary-evolution simulatiONs) incorporates full stellar structure and evolution modeling for single and binary-star population synthesis. The code is modular and allows the user to specify initial population properties and adopt choices that determine how stellar evolution proceeds. Populations are simulated with the use of MESA (ascl:1010.083) evolutionary tracks for single, non-interacting, and interacting binaries organized in grids. Machine-learning methods are incorporated and applied on the grids for classification and various interpolation calculations, and the development of irregular grids guided by active learning, for computational efficiency.

[ascl:2210.020]
ixpeobssim: Imaging X-ray Polarimetry Explorer simulator and analyzer

Baldini, Luca; Bucciantini, Niccolò; Lalla, Niccolò Di; Ehlert, Steven; Manfreda, Alberto; Negro, Michela; Omodei, Nicola; Pesce-Rollins, Melissa; Sgrò, Carmelo; Silvestri, Stefano

The simulation and analysis framework ixpeobssim was specifically developed for the Imaging X-ray Polarimetry Explorer (IXPE). It produces realistic simulated observations, in the form of event lists in FITS format, that also contain a strict superset of the information included in the publicly released IXPE data products. The framework's core simulation capabilities are complemented by post-processing applications that support the spatial, spectral, and temporal models needed for analysis of typical polarized X-ray sources, allowing implementation of complex, polarization-aware analysis pipelines. Where applicable, the data formats are consistent with the common display and analysis tools used by the community, *e.g.*, the binned count spectra can be fed into XSPEC (ascl:9910.005), along with the corresponding response functions, for doing standard spectral analysis. All ixpeobssim simulation and analysis tools are fully configurable via the command line.

[ascl:2210.021]
SHEEP: Machine Learning pipeline for astronomy classification

The photometric redshift-aided classification pipeline SHEEP uses ensemble learning to classify astronomical sources into galaxies, quasars and stars. It uses tabular data and also allows the use of sparse data. The approach uses SDSS and WISE photometry, but SHEEP can also be used with other types of tabular data, such as radio fluxes or magnitudes.

[ascl:2210.022]
MCCD: Multi-CCD Point Spread Function Modelling

MCCD (Multi-CCD) generates a Point Spread Function (PSF) model based on stars observations in the field of view. After defining the MCCD model parameters and running and validating the training, the model can recover the PSF at any position in the field of view. Written in Python, MCCD also calculates various statistics and can plot a random test star and its model reconstruction.

[ascl:2210.023]
BornRaytrace: Weak gravitational lensing effects simulator

BornRaytrace uses neural data compression of weak lensing map summary statistics to simulate weak gravitational lensing effects. It can raytrace through overdensity Healpix maps to return a convergence map, include shear-kappa transformation on the full sphere, and also include intrinsic alignments (NLA model).

[ascl:2210.024]
Faiss: Similarity search and clustering of dense vectors library

The Faiss library performs efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU.

[ascl:2210.025]
tvguide: Observability by TESS

tvguide determines whether stars and galaxies are observable by TESS. It uses an object's right ascension and declination and estimates the pointing of TESS's cameras using predicted spacecraft ephemerides to determine whether and for how long the object is observable with TESS. tvguide returns a file with two columns, the first the minimum number of sectors the target is observable for and the second the maximum.

[ascl:2210.026]
PGOPHER: Rotational, vibrational, and electronic spectra simulator

PGOPHER simulates and fits rotational, vibrational, and electronic spectra. It handles linear molecules and symmetric and asymmetric tops, including effects due to unpaired electrons and nuclear spin, with a separate mode for vibrational structure. The code performs many sorts of transitions, including Raman, multiphoton, and forbidden transitions. It can simulate multiple species and states simultaneously, including special effects such as perturbations and state dependent predissociation. Fitting can be to line positions, intensities, or band contours. PGOPHER uses a standard graphical user interface and makes comparison with, and fitting to, spectra from various sources easy. In addition to overlaying numerical spectra, it is also possible to overlay pictures from pdf files and even plate spectra to assist in checking that published constants are being used correctly.

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