PyMsOfa accesses the International Astronomical Union’s SOFA library (ascl:1403.026) from Python. It offers a wrapper package based on a foreign function library for Python (ctypes), a wrapper with the foreign function interface for Python calling C code (cffi), and a package directly written in pure Python codes from SOFA subroutines. PyMsOfa is suitable for the astrometric detection of habitable planets of the Closeby Habitable Exoplanet Survey (CHES) mission and for the frontier themes of black holes and dark matter related to astrometric calculations and other fields.

LimberJack.jl performs cosmological analyses of 2 point auto- and cross-correlation measurements from galaxy clustering, CMB lensing and weak lensing data. Written in Julia, it obtains gradients for its outputs faster than traditional finite difference methods, making the code greatly synergistic with gradient-based sampling methods such as Hamiltonian Monte Carlo. LimberJack.jl can efficiently exploring parameter spaces with hundreds of dimensions.

The Farmer contains photometry routines geared towards deep, multi-wavelength galaxy surveys. It fits simple parametric surface brightness profiles provided by The Tractor (ascl:1604.008) to measure precision photometry even in deeply crowded fields when provided with a suitable high resolution detection image. The Farmer has been used to build a number of galaxy survey catalogs including COSMOS202, SHELA, and H20.

SUNBIRD trains neural-network-based models for galaxy clustering. It also incorporates pre-trained emulators for different summary statistics, including galaxy two-point correlation function, density-split clustering statistics, and old-galaxy cross-correlation function. These models have been trained on mock galaxy catalogs, and were calibrated to work for specific samples of galaxies. SUNBIRD implements routines with PyTorch to train new neural-network emulators.

GRFolres performs simulations in modified theories of gravity. It is based on GRChombo (ascl:2306.039) and inherits all of the capabilities of the main GRChombo code, which makes use of the Chombo library (ascl:1202.008) for adaptive mesh refinement. The code implements the 4∂ST theory of modified gravity and the cubic Horndeski theory in (3+1)-dimensional numerical relativity. GRFolres can be used for stable gauge evolution, solving the modified energy and momentum constraints for initial conditions, and monitoring the constraint violation and calculating the energy densities associated with the different scalar terms in the action. It can also extract data for the tensor and scalar gravitational waveforms.

21cmEMU emulates 21cmFAST (ascl:1102.023) summary statistics, among them the 21-cm power spectrum, 21-cm global brightness temperature, IGM spin temperature, and neutral fraction. It also emulates the Thomson scattering optical depth and UV luminosity functions. With 21cmFAST installed, parameters can be supplied direction to 21cmEMU, and 21cmEMU can be used for, for example, analytic calculations of tau_e and UV luminosity functions. The code is included as an alternative simulator in 21cmMC (ascl:1608.017).

The folding pipeline PulsarX searches for pulsars. The code includes radio frequency interference mitigation, de-dispersion, folding, and parameter optimization, and supports both psrfits and filterbank data formats. The toolset has two implementations of the folding pipelines; one uses a brute-force de-dispersion algorithm, and the other an algorithm that becomes more efficient than the brute-force de-dispersion algorithm as the number of candidates increases. PulsarX is appropriate for large-scale pulsar surveys.

PhotochemPy finds the steady-state chemical composition of an atmosphere or evolves atmospheres through time. Given inputs such as the stellar UV flux and atmospheric temperature structure, the code creates a photochemical model of a planet's atmosphere. PhotochemPy is a distant fork of Atmos (ascl:2106.039). It provides a Python wrapper to Fortran source code but can also be used exclusively in Fortran.

FORECAST generates realistic astronomical images and galaxy surveys by forward modeling the output snapshot of any hydrodynamical cosmological simulation. It exploits the snapshot by constructing a lightcone centered on the observer's position; the code computes the observed fluxes of each simulated stellar element, modeled as a Single Stellar Population (SSP), in any chosen set of pass-band filters, including k-correction, IGM absorption, and dust attenuation. These fluxes are then used to create an image on a grid of pixels, to which observational features such as background noise and PSF blurring can be added. FORECAST provides customizable options for filters, size of the field of view, and survey parameters, thus allowing the synthetic images to be tailored for specific research requirements.

The non-parametric Jeans code GravSphere models discrete data and can be used to model dark matter distributions in galaxies. It can also recover the density ρ(r) and velocity anisotropy β(r) of spherical stellar systems, assuming only that they are in a steady state. Real or mock data are prepared by using the included binulator.py code; the repository also includes many examples for exploring the GravSphere's capabilities.

The CompressedFisher library tests whether Fisher forecasts using simulated components are converged. The library contains tools to compute standard Fisher estimates, estimate the level of bias due to the finite number of simulations, and compute the compressed Fisher information. Typical usage of CompressedFisher requires two ensembles of simulations: one set of simulations is given at the fiducial parameters (𝜃) to estimate the covariance matrix. The second is a set of simulated derivatives; these can either be in the form of realizations of the derivatives themselves or simulations evaluate at a set of point in the neighborhood of the fiducial point that the code can use to estimate the derivatives.

CosmoLED computes Hawking evaporation from black holes and set constraints on the fraction of black holes in dark matter. Based on ExoCLASS (ascl:1106.020), the code provides a DarkAges_LED module and C codes in class_LED to compute the evolution and energy deposition functions from LED black holes. Though CosmoLED is designed for large extra dimension black holes, it can also be used to study 4D black holes.

SolarAxionFlux quantifies systematic differences and statistical uncertainties in the calculation of the solar axion flux from axion-photon and axion-electron interactions. Determining the limitations of these calculations can be used to identify potential improvements and help determine axion model parameters more accurately.

LyaCoLoRe uses CoLoRe (ascl:2111.009) simulations to generate simulated Lyman alpha forest spectra. The code takes the output files from CoLoRe as an input, carries out several stages of processing, and produces realistic skewers of transmitted flux fraction as an output. The repository includes tools to tune the parameters within LyaCoLoRe's transformation, and to measure the 1D power spectrum of output skewers quickly.

DENSe enables Bayesian non-parametric inferences of densities of Poisson data counts. Its framework of stateless methods is written in Python, although it relies on NIFTy (ascl:1302.013, ascl:1903.008) for the heavy lifting. DENSe utilizes all available information in the data by modeling the inherent correlation structure using a Matérn kernel. The inference of the density from count data can be written in a single line of python code. The fitting method takes a multidimensional numpy array as input and returns multidimensional arrays of the same dimensions encoding the density field.

BUQO solves large-scale imaging inverse problems. It leverages probability concentration phenomena and the underlying convex geometry to formulate the Bayesian hypothesis test as a convex problem that is then efficiently solved by using scalable optimization algorithms. This allows scaling to high-resolution and high-sensitivity imaging problems that are computationally unaffordable for other Bayesian computation approaches.

PROSPECT infers cosmological parameters using profile likelihoods. It constructs an approximate profile likelihood from an MCMC and optimizes it using simulated annealing, a gradient-free stochastic optimization algorithm. It employs an automatic tuning of the step size parameter and binned covariance matrices from the MCMC to achieve efficient optimizations of the profile likelihood.

smops interpolates input sub-band model FITS images, such as those produced by WSClean (ascl:1408.023), into more finely channelized sub-band model FITS images, thus generating model images at a higher frequency resolution. It is a Python-based command line tool. For example, given input model FITS images initially created from sub-dividing a given bandwidth into four, smops can subdivide that bandwidth further, resulting in more finely channelized model images, to a specified frequency resolution. This smooths out the stepwise behavior of models across frequency, which can improve the results of self-calibration with such models.

prodimopy is an open-source Python package to read, analyze and plot modelling results of the radiation thermo-chemical disk code ProDiMo (PROtoplanetary DIsk MOdel, https://prodimo.iwf.oeaw.ac.at). It also includes tools to run ProDiMo in 1D slap model mode, to run simple ProDimo model grids and to interface ProDiMo with 1D and 2D disk codes (i.e. use input structure from hydrodynamic models).

prodimopy can also be used independently of ProDiMo (no ProDiMo installation is required) and hence is also useful to extract information from already available ProDiMo models (e.g. as input for other codes) or for model comparison.

The numerical code RoSSBi3D (Rotating Systems Simulation Code for Bi-fluids) is designed for protoplanetary discs study at 2D and 3D. It is a finite volume code which is second order in time, features self-gravity (2D), and uses an exact Riemann solver to account for discontinuities. This FORTRAN 90 code solves the fully compressible inviscid Euler, continuity and energy conservation equations in polar coordinates for an ideal gas orbiting a central object. Solid particles are treated as a pressureless fluid and interact with the gas through aerodynamic forces. The code works on high performance computers thanks to the MPI standard (CPU).

nemiss calculates neutrino emission from an astrophysical jet. nemiss works as part of the PLUTO-nemiss-rlos pipeline. PLUTO (ascl:1010.045) produces a hydrodynamical jet. Then, nemiss calculates beamed neutrino emission at each eligible cell along a given direction in space. Finally, rlos (ascl:1811.009) produces a synthetic neutrino image of the jet along the given direction, taking into consideration the finite nature of the speed of light.

FASMA delivers the atmospheric stellar parameters (effective temperature, surface gravity, metallicity, microturbulence, macroturbulence, and rotational velocity) based on the spectral synthesis technique. This technique relies on the comparison of synthetic spectra with observations to yield the best-fit parameters under a χ2 minimization process. FASMA also delivers chemical abundances of 13 elements. Written in Python, the code is wrapped around MOOG (ascl:1202.009) which calculates the synthetic spectra. FASMA includes two grids of models in MOOG readable format, Kurucz and marcs, that cover the parameter space for both dwarf and giant stars with metallicity limit of -5.0 dex.

pygwb analyzes laser interferometer data and designs a gravitational wave background (GWB) search pipeline. Its modular and flexible codebase is tailored to current ground-based interferometers such as LIGO Hanford, LIGO Livingston, and Virgo, but can be generalized to other configurations. It is based on GWpy (ascl:1912.016) and bilby (ascl:1901.011) for optimal integration with widely-used gravitational wave data analysis tools. pygwb also includes a set of scripts to analyze data and perform large-scale searches on a high-performance computing cluster efficiently.

CosmoLattice performs lattice simulations of field dynamics in an expanding universe. The code can simulate the dynamics of interacting scalar field theories, Abelian U(1) gauge theories, and non-Abelian SU(2) gauge theories, either in flat spacetime or an expanding FLRW background, including the case of self-consistent expansion sourced by the fields themselves. It can also compute gravitational waves sourced by U(1) Abelian Gauge fields. The CosmoLattice platform can implement any system of dynamical equations suitable for discretization on a lattice, as it introduces its own language describing fields and operations between them, and hence can implement new libraries to solve arbitrary field problems (related or not to cosmology).

PIPPIN (PDI pipeline for NACO data) reduces the polarimetric observations made with the VLT/NACO instrument. It applies the Polarimetric Differential Imaging (PDI) technique to distinguish the polarized, scattered light from the (largely) un-polarized, stellar light. As a result, circumstellar dust can be uncovered. PIPPIN appropriately handles various instrument configurations, including half-wave plate and de-rotator usage, Wollaston beam-splitter, and wiregrid observations. As part of the PDI reduction, PIPPIN performs various levels of corrections for instrumental polarization and crosstalk.

FPFS (Fourier Power Function Shaplets) is a fast, accurate shear estimator for the shear responses of galaxy shape, flux, and detection. Utilizing leading-order perturbations of shear (a vector perturbation) and image noise (a tensor perturbation), the code determines shear and noise responses for both measurements and detections. Unlike methods that distort each observed galaxy repeatedly, the software employs analytical shear responses of select basis functions, including Shapelets basis and peak basis. FPFS is efficient and can process approximately 1,000 galaxies within a single CPU second, and maintains a multiplicative shear estimation bias below 0.5% even amidst blending challenges.

Hi-COLA runs fast approximate N-body simulations of non-linear structure formation in reduced Horndeski gravity (Horndeski theories with luminal gravitational waves). It is generic with respect to the reduced Horndeski class. Given an input Lagrangian, Hi-COLA's front-end dynamically constructs the appropriate field equations and consistently solves for the cosmological background, linear growth, and screened fifth force of that theory. This is passed to the back-end, which runs a hybrid N-body simulation at significantly reduced computational and temporal cost compared to traditional N-body codes. By analyzing the particle snapshots, one can study the formation of structure through statistics such as the matter power spectrum.

IQRM implements the Inter-Quartile Range Mitigation (IQRM) interference flagging algorithm for radio pulsar and transient searches. This module provides only the algorithm that infers a channel mask from some spectral statistic that measures the level of RFI contamination in a time-frequency data block. It should be useful as a reference implementation to developers who wish to integrate IQRM into an existing pipeline or search code.

Tensiometer provides non-Gaussian tension estimators that extend GetDist (ascl:1910.018) capabilities to test the level of agreement or disagreement between different posterior distributions by using kernel density estimates. The code has been used to study the level of internal agreement between different measurements of the clustering of cosmological structures from the Dark Energy Survey and the Planck satellite.

MONDPMesh provides a particle-mesh method to calculate the time evolution of an system of point masses under modified gravity, namely the AQUAL formalism. This is done by transforming the Poisson equation for the potential into a system of four linear PDEs, and solving these using fast Fourier transforms. The accelerations on the point masses are calculated from this potential, and the system is propagated using Leapfrog integration. The time complexity of the code is O(N⋅p⋅log(p)) for p pixels and N particles, which is the same as for a Newtonian particle-mesh code.

The NEOexchange web portal and Target and Observation Manager ingests solar system objects, including Near-Earth Object (NEO) candidates from the Minor Planet Center, schedules observations on the Las Cumbres Observatory global telescope network and reduces, displays, and analyzes the resulting data. NEOexchange produces calibrated photometry from the imaging data and uses Source Extractor (ascl:1010.064) and SCAMP (ascl:1010.063) to perform object detection and astrometric fitting and calviacat (ascl:2207.015) to perform photometric calibration against photometric catalogs. It also has the ability to perform image registration and subtraction using SWARP (ascl:1010.068) and HOTPANTS (ascl:1504.004) and image stacking, alignment, and faint feature detection using gnuastro (ascl:1801.009).

The KvW code applies the Kwee Van Woerden (KvW) method for eclipse or transit minimum timing, with an improved error calculation that avoids underestimated errors in minimum times that may appear in the original method. This is particularly the case for low-noise eclipse or transit lightcurves from space or from modern ground instrumentation. The code requires an input light curve of near-equidistant points that contains only the eclipse, without any off-eclipse points, and is available in python and IDL. Both implementaitons return an eclipse minimum time with its error and provide optional text output and plots, as well as several levels of debug information.

atlas-fit is a python tool to amend the results of [spectroflat] with calibration against a solar atlas. I.e., data for wavelength calibration and continuum-correction is genereted from flat field information and selected solar atlantes

Spectroflat is a generic python calibration library for spectro-polarimetric data. It can be plugged into existing python based data reduction pipelines or used as a standalone calibration and performance ananlzsis tool.
It includes smile distortion correction and flat field extraction.

The landscape of high- and ultra-high-energy astrophysics has changed in the last decade, largely due to the inflow of data collected by large-scale cosmic-ray, gamma-ray, and neutrino observatories. At the dawn of the multimessenger era, the interpretation of these observations within a consistent framework is important to elucidate the open questions in this field. CRPropa 3.2 is a Monte Carlo code for simulating the propagation of high-energy particles in the Universe. This version represents a major leap forward, significantly expanding the simulation framework and opening up the possibility for many more astrophysical applications. This includes, among others: efficient simulation of high-energy particles in diffusion-dominated domains, self-consistent and fast modelling of electromagnetic cascades with an extended set of channels for photon production, and studies of cosmic-ray diffusion tensors based on updated coherent and turbulent magnetic-field models. Furthermore, several technical updates and improvements are introduced with the new version, such as: enhanced interpolation, targeted emission of sources, and a new propagation algorithm (Boris push). The detailed description of all novel features is accompanied by a discussion and a selected number of example applications.

The IDL code Special-Blurring compares models of quantum-foam-induced blurring with the full dataset of gamma-ray burst localizations available from the NASA High Energy Astrophysics Science Research Archive (as of 1 November 2022). This includes GRB221009A, which was especially bright and detected in extremely high energy TeV gamma-rays. An upper limit of the parameter alpha (giving the maximal strength of quantum blurring) can be entered, which is scaled in the model of blurring (called "Phi") operating much like "seeing" from the ground in the optical, and those calculations are plotted against the observations.

VCAL-SPHERE, for VIP-based Calibration of VLT/SPHERE data, is a versatile pipeline for high-contrast imaging of exoplanets and circumstellar disks. The pipeline covers all steps of data reduction, including raw calibration, pre-processing and post-processing (i.e., modeling and subtraction of the stellar halo), for the IFS, IRDIS-DBI and IRDIS-CI modes (and combinations thereof) of the VLT instrument SPHERE. The three main steps of the reduction correspond to different modules, where the first follows the recommended EsoRex (ascl:1504.003) workflow and associated recipes with occasional inclusion of VIP (ascl:1603.003) routines (e.g., for PCA-based sky subtraction), while the other two stages fully rely on the VIP toolbox. Although the default parameters of the pipeline should yield a good reduction in most cases, these can be tuned using JSON parameter files for each stage of the pipeline for optimal reduction of specific datasets.

The graphical user interface Wavelength Calibrator facilitates wavelength calibration. Although developed for astronomical data reduction, it can also be used in any place where wavelength calibration is needed.

GRIZZLY simulates reionization using a 1D radiative transfer scheme. The code enables the efficient exploration of the parameter space for evaluating 21cm brightness temperature fluctuations near the cosmic dawn. GRIZZLY builds upon the BEARS algorithm for generating simulated reionization maps with density and velocity fields, which are useful for profiling dark matter halos and cosmological density fields.

AI-Feynman fits analytical expressions to data sets via symbolic regression, mapping the target variable to different features supplied in the data array. Using a neural network with constraints in the number of parameters utilized, the code provides the ability to obtain analytical expressions for normalized features that are used to predict a Pareto-optimal target. AI-Feynman is robust in handling noisy data, recursively generating multidimensional symbolic expressions that match data from an unknown functions.

riptide implements the Fast Folding Algorithm (FFA) to identify periodic signals from time series data. In order to identify faint pulsars, the code provides access to a library of functions and classes for processing dedispersed radio signals. The FFA approaches the theoretical optimum for sensitivity to periodic signals regardless of pulse period and duty cycle.

IQRM-APOLLO cleans narrow-band radio frequency interference (RFI) using the Inter-Quartile Range Mitigation (IQRM) algorithm. By masking this interference, the code reduces the number of false positive pulsar candidates and increases sensitivity for pulsar detection. The IQRM algorithm is an outlier detection algorithm that is both non-parametric and robust to the presences of trends in time series data. Using short-duration data blocks, IQRM-APOLLO computes a spectral statistic that correlates with the presence of RFI, removing high outliers from the input signal.

clfd (clean folded data) implements GPU-accelerated smart interference removal algorithms to be used on folded pulsar search and pulsar timing data. The code converts each source profile to a small set of representative features, flagging outliers in the resulting feature space. clfd further visualizes the outlier flagging process, as well as the resulting two-dimensional time-frequency mask that is applied to the clean archive. The code provides access to cleaning algorithms that were initially developed for the High Time Resolution Universe (HTRU) survey which found several pulsars.

zCluster measures galaxy cluster photometric redshifts using data from broadband photometry in large public surveys, given a priori knowledge of the cluster position. The code retrieves and uses redshift probability distributions in order to create a projected two-dimensional density map of a targeted galaxy cluster, which is later convolved with a Gaussian kernel to smooth the map. zCluster also produces photometric redshift estimates and galaxy density maps for any point in the sky using the included zField tool.

MAGPy-RV (Modelling stellar Activity with Gaussian Processes in Radial Velocity) models data with Gaussian Process regression and affine invariant Monte Carlo Markov Chain parameter searching. Developed to model intrinsic, quasi-periodic variations induced by the host star in radial velocity (RV) surveys for the detection of exoplanets and the accurate measurements of their orbital parameters and masses, it now includes a variety of kernels and models and can be applied to any timeseries analysis. MAGPy-RV includes publication level plotting, efficient posterior extraction, and export-ready LaTeX results tables. It also handles multiple datasets at once and can model offsets and systematics from multiple instruments. MAGPy-RV requires no external dependencies besides basic python libraries and corner (ascl:1702.002).

The DustPyLib library contains auxiliary modules for the dust evolution software DustPy (ascl:2207.016), which simulates the evolution of dust and gas in protoplanetary disks. DustPyLib includes interfaces to radiative transfer codes and modules with extensions to the DustPy defaults.

q3dfit performs PSF decomposition and spectral analysis for high dynamic range JWST IFU observations, allowing the user to create science-ready maps of relevant spectral features. The software takes advantage of the spectral differences between quasars and their host galaxies for maximal-contrast subtraction of the quasar point-spread function (PSF) to reveal and characterize the faint extended emission of the host galaxy. Host galaxy emission is carefully fit with a combination of stellar continuum, emission and absorption of dust and ices, and ionic and molecular emission lines.

wwz provides a python3 implementation of the Foster weighted wavelet z-transform, a wavelet-based method for periodicity analysis of unevenly sampled data.

lcsim creates artificial light curves using two algorithms. The first simulates Gaussian distributed light curves following a specific power spectral density (PSD) freely selectable by the user. The second algorithm simulates light curves following a specific PSD and matching a specific probability density function (PDF). The package provides methods to resample the simulated light curves and add "observational" noise. Furthermore, the package provides an interface to a SQLite3-based database to store and access the simulations.

celerite2 is a re-write of celerite (ascl:1709.008), an algorithm for fast and scalable Gaussian Process (GP) Regression in one dimension. celerite2 improves numerical stability and integration with various machine learning frameworks. The implementation includes interfaces in Python and C++, with full support for PyMC (ascl:1610.016) and JAX (ascl:2111.002).