[ascl:2405.023]
raccoon: Radial velocities and Activity indicators from Cross-COrrelatiON with masks

raccoon implements the cross-correlation function (CCF) method. It builds weighted binary masks from a stellar spectrum template, computes the CCF of stellar spectra with a mask, and derives radial velocities (RVs) and activity indicators from the CCF. raccoon is mainly implemented in Python 3; it also uses some Fortran subroutines that are called from Python.

[ascl:2405.024]
ndcube: Multi-dimensional contiguous and non-contiguous coordinate-aware arrays

Ryan, Daniel F.; Mumford, Stuart; Barnes, Will T.; Baruah, Ankit Kumar; Bhope, Adwait; Buchlin, Éric; Freij, Nabil; Ginsburg, Adam; Hayes, Laura A.; Homeier, Derek; Hughes, J. Marcus; Lowder, Chris; O'Steen, Richard; Pellorce, Baptiste; Robitaille, Thomas; Sharma, Yash; Stansby, David; Shih, Albert Y.; Tollerud, Erik; Weberg, Micah J.; West, Matthew J.

ndcube manipulates, inspects, and visualizes multi-dimensional contiguous and non-contiguous coordinate-aware data arrays. A sunpy (ascl:1401.010) affiliated package, it combines data, uncertainties, units, metadata, masking, and coordinate transformations into classes with unified slicing and generic coordinate transformations and plotting and animation capabilities. ndcube handles data of any number of dimensions and axis types (*e.g.*, spatial, temporal, and spectral) whose relationship between the array elements and the real world can be described by World Coordinate System (WCS) translations.

[ascl:2405.025]
CosmoPower: Machine learning-accelerated Bayesian inference

CosmoPower develops Bayesian inference pipelines that leverage machine learning to solve inverse problems in science. While the emphasis is on building algorithms to accelerate Bayesian inference in cosmology, the implemented methods allow for their application across a wide range of scientific fields. CosmoPower provides neural network emulators of matter and Cosmic Microwave Background power spectra, which can replace Boltzmann codes such as CAMB (ascl:1102.026) or CLASS (ascl:1106.020) in cosmological inference pipelines, to source the power spectra needed for two-point statistics analyses. This provides orders-of-magnitude acceleration to the inference pipeline and integrates naturally with efficient techniques for sampling very high-dimensional parameter spaces.

[ascl:2406.001]
GAStimator: Python MCMC gibbs-sampler with adaptive stepping

GAStimator implements a Python MCMC Gibbs-sampler with adaptive stepping. The code is simple, robust, and stable and well suited to high dimensional problems with many degrees of freedom and very sharp likelihood features. It has been used extensively for kinematic modeling of molecular gas in galaxies, but is fully general and may be used for any problem MCMC methods can tackle.

[ascl:2406.002]
SRF: Scaling Relations Finder

Scaling Relations Finder finds the scaling relations between magnetic field properties and observables for a model of galactic magnetic fields. It uses observable quantities as input: the galaxy rotation curve, the surface densities of the gas, stars and star formation rate, and the gas temperature to create galactic dynamo models. These models can be used to estimate parameters of the random and mean components of the magnetic field, as well as the gas scale height, root-mean-square velocity and the correlation length and time of the interstellar turbulence, in terms of the observables.

[ascl:2406.003]
SMART: Spectral energy distribution (SED) fitter

SMART (Spectral energy distributions Markov chain Analysis with Radiative Transfer models) implements a Bayesian Markov chain Monte Carlo (MCMC) method to fit the ultraviolet to millimeter spectral energy distributions (SEDs) of galaxies exclusively with radiative transfer models. The models constitute four types of pre-computed libraries, which describe the starburst, active galactic nucleus (AGN) torus, host galaxy and polar dust components.

[ascl:2406.004]
candl: Differentiable likelihood framework for analyzing CMB power spectrum measurements

candl (CMB Analysis With A Differentiable Likelihood) analyzes CMB power spectrum measurements using a differentiable likelihood framework. It is compatible with JAX (ascl:2111.002), though JAX is optional, allowing for fast and easy computation of gradients and Hessians of the likelihoods, and candl provides interface tools for working with other cosmology software packages, including Cobaya (ascl:1910.019) and MontePython (ascl:1805.027). The package also provides auxiliary tools for common analysis tasks, such as generating mock data, and supports the analysis of primary CMB and lensing power spectrum data.

[ascl:2406.005]
Lenser: Measure weak gravitational flexion

Lenser estimates weak gravitational lensing signals, particularly flexion, from real survey data or realistically simulated images. Lenser employs a hybrid of image moment analysis and an Analytic Image Modeling (AIM) analysis. In addition to extracting flexion measurements by fitting a (modified Sérsic) model to a single image of a galaxy, Lenser can do multi-band, multi-epoch fitting. In multi-band mode, Lenser fits a single model to multiple postage stamps, each representing an exposure of a single galaxy in a particular band.

[ascl:2406.006]
anzu: Measurements and emulation of Lagrangian bias models for clustering and lensing cross-correlations

The anzu package offers two independent codes for hybrid Lagrangian bias models in large-scale structure. The first code measures the hybrid "basis functions"; the second takes measurements of these basis functions and constructs an emulator to obtain predictions from them at any cosmology (within the bounds of the training set). anzu is self-contained; given a set of N-body simulations used to build emulators, it measures the basis functions. Alternatively, given measurements of the basis functions, anzu should in principle be useful for constructing a custom emulator.

[ascl:2406.007]
CARDiAC: Anisotropic Redshift Distributions in Angular Clustering

CARDiAC (Code for Anisotropic Redshift Distributions in Angular Clustering) computes the impact of anisotropic redshift distributions on a wide class of angular clustering observables. It supports auto- and cross-correlations of galaxy samples and cosmic shear maps, including galaxy-galaxy lensing. The anisotropy can be present in the mean redshift and/or width of Gaussian distributions, as well as in the fraction of galaxies in each component of multi-modal distributions. Templates of these variations can be provided by the user or simulated internally within the code.

[ascl:2406.008]
sphereint: Integrate data on a grid within a sphere

sphereint calculates the numerical volume in a sphere. It provides a weight for each grid position based on whether or not it is in (weight = 1), out (weight = 0), or partially in (weight in between 0 and 1) a sphere of a given radius. A cubic cell is placed around each grid position and the volume of the cell in the sphere (assuming a flat surface in the cell) is calculated and normalized by the cell volume to obtain the weight.

[ascl:2406.009]
CBiRd: Bias tracers In Redshift space

Zhang, Pierre; d'Amico, Guido; Gleyzes, Jerome; Beutler, F.; Colas, T.; Gil-Marin, H.; Kokron, N.; Lewandowski, M.; Markovic, D.; Perko, A.

CBiRd (Code for Bias tracers In Redshift space) provides correlators in the Effective Field Theory of Large-Scale Structure (EFTofLSS) in a ready-to-use pipeline for cosmological analysis of galaxy-redshift surveys data. It provides a core calculation package (C++BiRd), a Python implementation of a Taylor expansion of the power spectrum around a reference cosmology for efficient evaluation (TBiRd), and libraries to correct for observational systematics. CBiRd also provides MCMC samplers (MCBiRd) for a power spectrum and bispectrum analysis of galaxy-redshift surveys data based on emcee (ascl:1303.002), and can provide an earlybird pass to explore the cosmos with LSS surveys.

[ascl:2406.010]
PRyMordial: Precise computations of BBN within and beyond the Standard Model

PRyMordial offers fast and precise evaluation of both the Big Bang Nucleosynthesis (BBN) light-element abundances and the effective number of relativistic degrees of freedom. It can be used within and beyond the Standard Model. The package calculates N_{eff} and helium-4, deuterium, helium-3 and lithium-7 abundances. PRyMordial corrects for QED plasma effects, neutron lifetime, and incomplete neutrino decoupling, and includes an optional module that re-elaborates all the ODE systems of the code in Julia.

[ascl:2406.011]
CTC: Color transformations calculator

Color transformations calculator determines the magnitude of a galaxy in a needed photometric band, given its color and magnitude in the original band. It supports various optical and near intrared surveys, including SDSS, DECaLS, DELVE, UKIDSS, VHS, and VIKING, and provides conversions for both total and aperture magnitudes with apertures of 1.5", 2" or 3" diameters. The source code, useful for performing bulk calculations, is available in Python and IDL; the calculator is also offered as a web service.

[ascl:2406.012]
QMC: Quadratic Monte Carlo

Quadratic Monte Carlo generates ensembles of models and confines fitness landscapes without relying on linear stretch moves; it works very efficiently for ring potential and Rosenbrock density. The method is general and can be implemented into any existing MC software, requiring only a few lines of code.

[ascl:2406.013]
AAD: ALeRCE Anomaly Detector

Perez-Carrasco, Manuel; Cabrera-Vives, Guillermo; Hernandez-García, Lorena; Förster, F.; Sanchez-Saez, Paula; Muñoz Arancibia, Alejandra M.; Arredondo, Javier; Astorga, Nicolás; Bauer, Franz E.; Bayo, Amelia; Catelan, M.; Dastidar, Raya; Estévez, P. A.; Lira, Paulina; Pignata, Giuliano

The ALeRCE anomaly detector cross-validates six anomaly detection algorithms for three classes (transient, periodic, and stochastic) of anomalous sources within the Zwicky Transient Facility (ZTF) data stream using the ALeRCE light curve features. A machine and deep learning-based framework is used for anomaly detection. For each class, a distinct anomaly detection model is constructed using only information about the known objects (*i.e.*, inliers) for training. An anomaly score is computed using the probabilities to determine whether the light curve corresponds to a transient, stochastic, or periodic nature.

[ascl:2406.014]
EVA: Excess Variability-based Age

EVA (Excess Variability-based Age) computes the VarX values and VarX90 ages for a given list of stars. The package retrieves information from Gaia, performs basic var90 calculations, then calculates the age of the group in a given band or overall (by combining all three bands). EVA then analyzes and plots the results.

[ascl:2406.015]
FLORAH: Galaxy merger tree generator with machine learning

FLORAH generates the assembly history of halos using a recurrent neural network and normalizing flow model. The machine-learning framework can be used to combine multiple generated networks that are trained on a suite of simulations with different redshift ranges and mass resolutions. Depending on the training, the code recovers key properties, including the time evolution of mass and concentration, and galaxy stellar mass versus halo mass relation and its residuals. FLORAH also reproduces the dependence of clustering on properties other than mass, and is a step towards a machine learning-based framework for planting full merger trees.

[ascl:2406.016]
BiaPy: Bioimage analysis pipeline builder

Franco-Barranco, Daniel; Andrés-San Román, Jesús A.; Hidalgo-Cenalmor, Ivan; Backová, Lenka; González-Marfil, Aitor; Caporal, Clément; Chessel, Anatole; Gómez-Gálvez, Pedro; Escudero, Luis M.; Wei, Donglai; Muñoz-Barrutia, Arrate; Arganda-Carreras, Ignacio

BiaPy provides deep-learning workflows for a large variety of image analysis tasks, including 2D and 3D semantic segmentation, instance segmentation, object detection, image denoising, single image super-resolution, self-supervised learning and image classification. Though developed specifically for bioimages, it can be used for watershed-based instance segmentation for friends-of-friends proto-haloes.

[ascl:2406.017]
ytree: yt-based merger-tree code

ytree reads and works with merger tree data from multiple formats. An extension of yt (ascl:1011.022), which can analyze snapshots from cosmological simulations, ytree can be thought of as the yt of merger trees. ytree's online documentation lists supported formats; support for additional formats can be added, as in principle, any type of tree-like data where an object has one or more ancestors and a single descendant can be supported.

[ascl:2406.018]
SuperLite: Spectral synthesis code for interacting transients

SuperLite produces synthetic spectra for astrophysical transient phenomena affected by circumstellar interaction. It uses Monte Carlo methods and multigroup structured opacity calculations for semi-implicit, semirelativistic radiation transport in high-velocity shocked outflows, and can reproduce spectra of typical Type Ia, Type IIP, and Type IIn supernovae. SuperLite also generates high-quality spectra that can be compared with observations of transient events, including superluminous supernovae, pulsational pair-instability supernovae, and other peculiar transients.

[ascl:2406.019]
MBE: Magnification bias estimation

Magnification bias estimation estimates magnification bias for a galaxy sample with a complex photometric selection for the example of SDSS BOSS. The code works for CMASS and the LOWZ, z1 and z3 samples. A template for applying the approach to other surveys is included; requirements include a galaxy catalog that provides magnitudes (used for photometric selection) and the exact conditions used for the photometric selection.

[ascl:2406.020]
LeHaMoC: Leptonic-Hadronic Modeling Code for high-energy astrophysical sources

LeHaMoC simulates high-energy astrophysical sources. It simulates the behavior of relativistic pairs, protons interacting with magnetic fields, and photons in a spherical region. The package contains numerous physical processes, including synchrotron emission and self-absorption, inverse Compton scattering, photon-photon pair production, and adiabatic losses. It also includes proton-photon pion production, proton-photon (Bethe-Heitler) pair production, and proton-proton collisions. LeHaMoC can model expanding spherical sources with a variable magnetic field strength. In addition, three types of external radiation fields can be defined: grey body or black body, power-law, and tabulated.

[ascl:2406.021]
photochem: Chemical model of planetary atmospheres

Photochem models the photochemical and climate composition of a planet's atmosphere. It takes inputs such as the stellar UV flux and atmospheric temperature structure to find the steady-state chemical composition of an atmosphere, or evolve atmospheres through time. Photochem also contains 1-D climate models and a chemical equilibrium solver.

[ascl:2406.022]
phazap: Low-latency identification of strongly lensed signals

Phazap post-processes gravitational-wave (GW) parameter estimation data to obtain the phases and polarization state of the signal at a given detector and frequency. It is used for low-latency identification of strongly lensed gravitational waves via their phase consistency by measuring their distance in the detector phase space. Phazap builds on top of the IGWN conda enviroment which includes the standard GW packages LALSuite (ascl:2012.021) and bilby (ascl:1901.011), and can be applied beyond lensing to test possible deviations in the phase evolution from modified theories of gravity and constrain GW birefringence.

[ascl:2406.023]
AARD: Automatic detection of solar active regions

This python code automatically detects solar active regions (AR). Based on morphological operation and region growing, it uses synoptic magnetograms from SOHO/MDI and SDO/HMI and calculates the parameters that characterize each AR, including the latitude and longitude of the flux-weighted centroid of two polarities and the whole AR, the area, and the flux of each polarity, and the initial and final dipole moments.

[ascl:2406.024]
GRINN: Gravity Informed Neural Network for studying hydrodynamical systems

GRINN (Gravity Informed Neural Network) solves the coupled set of time-dependent partial differential equations describing the evolution of self-gravitating flows in one, two, and three spatial dimensions. It is based on physics informed neural networks (PINNs), which are mesh-free and offer a fundamentally different approach to solving such partial differential equations. GRINN has solved for the evolution of self-gravitating, small-amplitude perturbations and long-wavelength perturbations and, when modeling 3D astrophysical flows, provides accuracy on par with finite difference (FD) codes with an improvement in computational speed.

[ascl:2406.025]
PowerSpecCovFFT: FFTLog-based computation of non-Gaussian analytic covariance of galaxy power spectrum multipoles

PowerSpecCovFFT compute the non-Gaussian (regular trispectrum and its shot noise) part of the analytic covariance matrix of the redshift-space galaxy power spectrum multipoles using an FFTLog-based method. The galaxy trispectrum is based on a tree-level standard perturbation theory but with a slightly different galaxy bias expansion. The code computes the non-Gaussian covariance of the power spectrum monopole, quadrupole, hexadecapole, and their cross-covariance up to kmax ~ 0.4 h/Mpc.

[ascl:2406.026]
Faceted-HyperSARA: Parallel faceted imaging in radio interferometry

Thouvenin, Pierre-Antoine; Dabbech, Arwa; Jiang, Ming; Abdulaziz, Abdullah; Thiran, Jean-Philippe; Jackson, Adrian; Wiaux, Yves

Faceted-HyperSARA images radio-interferometric wideband intensity data. Written in MATLAB, the library offers a collection of utility functions and scripts from data extraction from an RI measurement set MS Table to the reconstruction of a wideband intensity image over the field of view and frequency range of interest. The code achieves high precision imaging from large data volumes and supports data dimensionality reduction via visibility gridding and estimation of the effective noise level when reliable noise estimates are not available. Faceted-HyperSASA also corrects the w-term via w-projection and incorporates available compact Fourier models of the direction dependent effects (DDEs) in the measurement operator.

[ascl:2406.027]
phi-GPU: Parallel Hermite Integration on GPU

Berczik, Peter; Nitadori, Keigo; Zhong, Shiyan; Spurzem, Rainer; Hamada, Tsuyoshi; Wang, Xiaowei; Berentzen, Ingo; Veles, Alexander; Ge, Wei

The phi-GPU (Parallel Hermite Integration on GPU) high-order N-body parallel dynamic code uses the fourth-order Hermite integration scheme with hierarchical individual block time-steps and incorporates external gravity. The software works directly with GPU, using only NVIDIA GPU and CUDA code. It creates numerical simulations and can be used to study galaxy and star cluster evolution.

[ascl:2406.028]
Redback: Bayesian inference package for fitting electromagnetic transients

Redback provides end-to-end interpretation and parameter estimation of electromagnetic transients. Using data downloaded by the code or provided by the user, the code processes the data into a homogeneous transient object. Redback implements several different types of electromagnetic transients models, ranging from simple analytical models to numerical surrogates, fits models implemented in the package or provided by the user, and plots lightcurves. The code can also be used as a tool to simulate realistic populations without having to fit anything, as models are implemented as functions and can be used to simulate populations. Redback uses Bilby (ascl:1901.011) for sampling and can easily switch samplers and likelihoods.

[ascl:2406.030]
AutoPhOT: Rapid publication-quality photometry of transients

AutoPhOT (AUTOmated Photometry Of Transients) produces publication-quality photometry of transients quickly. Written in Python 3, this automated pipeline's capabilities include aperture and PSF-fitting photometry, template subtraction, and calculation of limiting magnitudes through artificial source injection. AutoPhOT is also capable of calibrating photometry against either survey catalogs (*e.g.*, SDSS, PanSTARRS) or using a custom set of local photometric standards.

[submitted]
Exovetter

Exovetter is an open-source, pip-installable python package which calculates metrics on high cadence time series photometry to distinguish between exoplanet transit signals and false positives. The package standardizes the implementation of metrics developed for the TESS, Kepler, and K2 missions such as Odd-Even, Multiple Event Statistic, and Centroid Offset (see “Planetary Candidates Observed by Kepler. VIII.”, Thompson et al. 2018.). Metrics can be run individually or together as part of a pipeline. Exovetter also includes several visualizations to further evaluate the transits and metrics.

[ascl:2406.029]
WinNet: Flexible, multi-purpose, single-zone nuclear reaction network

Reichert, M.; Winteler, C.; Korobkin, O.; Arcones, A.; Bliss, J.; Eichler, M.; Frischknecht, U.; Fröhlich, C.; Hirschi, R.; Jacobi, M.; Kuske, J.; Martínez-Pinedo, G.; Martin, D.; Mocelj, D.; Rauscher, T.; Thielemann, F. K.

WinNet, a single zone nuclear reaction network, calculates many different nucleosynthesis processes, including r-process, nup-process, and explosive nucleosynthesis, and many more). It reads in a user-defined file with runtime parameters, then chooses the evolution mode, which is dependent on temperature. The temperature, density, and neutrino quantities are updated, after which the reaction network equations are solved numerically. If convergence is not achieved, the step size is halved and the iteration is repeated. Once convergence is reached, the output is generated and the time is evolved; the final output such as the final abundances and mass fractions are written.

[ascl:2407.001]
MAKEE: MAuna Kea Echelle Extraction

MAKEE (MAuna Kea Echelle Extraction) reduces data from the HIRES and ESI instruments at Keck Observatory. It is optimized for the spectral extraction of single, unresolved point sources and is designed to run non-interactively using a set of default parameters. Taking the raw HIRES FITS files as input, the code determines the position (or trace) of each echelle order, defines the object and background extraction boundaries, optimally extracts a spectrum for each order, and computes wavelength calibrations. MAKEE produces FITS format "spectral images" (each row is a separate echelle order spectrum) and the data values are in arbitrary (relative) flux units. MAKEE will reduce data from all HIRES formats, including the single CCD format, the single CCD with Red and UV cross dispersers, and the current 3 CCD system. It can handle a variety of pixel binnings, including 1x1, 1x2, 1x4 (column x row).

[ascl:2407.002]
pyFAT: Python Fully Automated TiRiFiC

Python Fully Automated TiRiFiC (pyFAT) wraps around the tilted ring fitting code (TiRiFiC, ascl:1208.008) to fully automate the process of fitting simple tilted ring models to line emission cubes. pyFAT is the successor to the IDL/GDL FAT (ascl:1507.011) code and offers improved handling and fitting as well as several new features. PyFAT fits simple rotationally symmetric discs with asymmetric warps and surface brightness distributions, providing a base model that can can be used in TiRiFiC to explore large scale motions. pyFAT delivers much more control over the fitting procedure, which is made possible by the new modular setup and the use of omegaconf for the input and default settings.

[ascl:2407.003]
pycosie: Python analysis code used on Technicolor Dawn

pycosie is analysis code used for Technicolor Dawn (TD), a Gadget-3 derived cosmological radiative SPH simulation suite. The target analyses are to complement what is done with TD and other analysis software in its suite. pycosie creates power spectrum from generated Lyman-alpha forests spectra, links absorbers to potential host galaxies, grids gas information for each galaxy, and reads specific output files from software such as Rockstar (ascl:1210.008) and SKID (ascl:1102.020).

[ascl:2407.004]
Forklens: Deep learning weak lensing shear

Zhang, Zekang; Shan, Huanyuan; Li, Nan; Wei, Chengliang; Yao, Ji; Ban, Zhang; Fang, Yuedong; Guo, Qi; Liu, Dezi; Li, Guoliang; Lin, Lin; Li, Ming; Li, Ran; Li, Xiaobo; Luo, Yu; Meng, Xianmin; Nie, Jundan; Qi, Zhaoxiang; Qiu, Yisheng; Shao, Li; Tian, Hao; Wang, Lei; Wang, Wei; Xian, Jingtian; Xu, Youhua; Zhang, Tianmeng; Zhang, Xin; Zhou, Zhimin

Forklens measures weak gravitational lensing signal using a deep-learning methoe. It measures galaxy shapes (shear) and corrects the smearing of the point spread function (PSF, an effect from either/both the atmosphere and optical instrument). It contains a custom CNN architecture with two input branches, fed with the observed galaxy image and PSF image, and predicts several features of the galaxy, including shape, magnitude, and size. Simulation in the code is built directly upon GalSim (ascl:1402.009).

[ascl:2407.005]
BaCoN: BAyesian COsmological Network

BaCoN (BAyesian COsmological Network) trains and tests Bayesian Convolutional Neural Networks in order to classify dark matter power spectra as being representative of different cosmologies, as well as to compute the classification confidence. It supports the following theories: LCDM, wCDM, f(R), DGP, and a randomly generated class. Additional cosmologies can be easily added.

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