cosmic_variance calculates the cosmic variance during the Epoch of Reionization (EoR) for the UV Luminosity Function (UV LF), Stellar Mass Function (SMF), and Halo Mass Function (HMF). The three functions in the package provide the output as the cosmic variance expressed in percentage. The code is written in Python, and simple examples that show how to use the functions are provided.

Kd-match matches stellar catalogs for which the transformation between the coordinate systems of the two catalogs is unknown and might include shearing. The code uses the ratio of sides as the invariant under a coordinate transformation and searches for several triangles with similar transformations by building quadrilaterals from sets of four objects in each catalog and calculating the ratio of areas of the triangles that comprise the quadrilaterals. The k-d tree accelerates this quadrilateral search dramatically and is significantly faster than the customary direct search over triangles.

The RePrimAnd library supports numerical simulations of general relativistic magnetohydrodynamics. It provides methods for recovering primitive variables such as pressure and velocity from the variables evolved in quasi-conservative formulations. Further, it provides a general framework for handling matter equations of state (EOS). Python bindings are automatically built together with the library, provided a Python3 installation containing the pybind11 package is detected. RePrimAnd also provides an (experimental) thorn that builds the library within an Einstein Toolkit (ascl:1102.014) environment using the ExternalLibraries mechanism.

Chem-I-Calc evaluates the chemical information content of resolved star spectroscopy. It takes advantage of the Fisher information matrix and the Cramér-Rao inequality to quickly calculate the Cramér-Rao lower bounds (CRLBs), which give the best theoretically achievable precision from a set of observations.

PlaSim is a climate model of intermediate complexity for Earth, Mars and other planets. It is written for a university environment, to be used to train the next GCM (general circulation model) developers, to support scientists in understanding climate processes, and to do fundamental research. In addition to an atmospheric GCM of medium complexity, PlaSim includes other compartments of the climate system such as, for example, an ocean with sea ice and a land surface with a biosphere. These other compartments are reduced to linear systems. In other words, PlaSim consists of a GCM with a linear ocean/sea-ice module formulated in terms of a mixed layer energy balance. The soil/biosphere module is introduced analoguously. Thus, working with PlaSim is like testing the performance of an atmospheric or oceanic GCM interacting with various linear processes, which parameterize the variability of the subsystems in terms of their energy (and mass) balances.

ART reconstructs log-probability distributions using Gaussian processes. It requires an existing MCMC chain or similar set of samples from a probability distribution, including the log-probabilities. Gaussian process regression is used for interpolating the log-probability for the rescontruction, allowing for easy resampling, importance sampling, marginalization, testing different samplers, investigating chain convergence, and other operations.

PyCactus contains tools for postprocessing data from numerical simulations performed with the Einstein Toolkit, based on the Cactus computational toolkit. The main package is PostCactus, which provides a high-level Python interface to the various data formats in a simulation folder. Further, the package SimRep allows the automatic creation of html reports for a simulation, and the SimVideo package allows the creation of movies visualizing simulation data.

shear-stacking calculates stacked shear profiles and tests based upon them, e.g. consistency for different slices of lensed background galaxies. The basic concept is that the lensing signal in terms of surface mass density (instead of shear) should be entirely determined by the properties of the lens sample and have no dependence on source galaxy properties.

The shapelens C++ library provides ways to load galaxies and star images from FITS files and catalogs and to analyze their morphology. The main purpose of this library is to make several weak-lensing shape estimators publicly available. All of them are based on the moments of the brightness distribution. The estimators include DEIMOS, for analytic deconvolution in moment space, DEIMOSElliptical, a practical implemention of DEIMOS with an automatically matched elliptical weight function, DEIMOSCircular, which is identical to DEIMOSElliptical but with a circular weight function, and others.

Skylens++ implements a Layer-based raytracing framework particularly well-suited for realistic simulations of weak and strong gravitational lensing. Source galaxies can be drawn from analytic models or deep space-based imaging. Lens planes can be populated with arbitrary deflectors, typically either from N-body simulations or analytic lens models. Both sources and lenses can be placed at freely configurable positions into the light cone, in effect allowing for multiple source and lens planes.

GUBAS (General Use Binary Asteroid Simulator) predicts binary asteroid system behaviors by implementing the Hou 2016 realization of the full two-body problem (F2BP). The F2BP models binary asteroid systems as two arbitrary mass distributions whose mass elements interact gravitationally and result in both gravity forces and torques. To account for these mass distributions and model the mutual gravity of the F2BP, GUBAS computes the inertia integrals of each body up to a user defined expansion order. This approach provides a recursive expression of the mutual gravity potential and represents a significant decrease in the computational burden of the F2BP when compared to other methods of representing the mutual potential.

PyROA models quasar light curves where the variability is described using a running optimal average (ROA), and parameters are sampled using Markov Chain Monte Carlo (MCMC) techniques using emcee (ascl:1303.002). Using a Bayesian approach, priors can be used on the sampled parameters. Currently it has three main uses: 1.) Determining the time delay between lightcurves at different wavelengths; 2.) Intercalibrating light curves from multiple telescopes, merging them into a single lightcurve; and 3.) Determining the time delay between images of lensed quasars, where the microlensing effects are also modeled. PyROA also includes a noise model, where there is a parameter for each light curve that adds extra variance to the flux measurments, to account for underestimated errors; this can be turned off if required. Example jupyter notebooks that demonstrate each of the three main uses of the code are provided.

AlignBandColors (ABC) aligns inter-color-band astronomical images to a 100th of a pixel accuracy using surrounding stars as guiding points. It has currently been tested with Sloan Digital Sky Survey (SDSS) Data Release 12 images, but is designed to be survey-independent. The code is part of the SpArcFiRe (ascl:2107.010) method.

SpArcFiRe takes as input an image of a galaxy in FITS, JPG, or PNG format, identifies spiral arms, and extracts structural information about the spiral arms. Pixels in each arm segment are listed, enabling image analysis on each segment. The automated method also performs a least-squares fit of a logarithmic spiral arc to the pixels in that segment, giving per-arc parameters, such as the pitch angle, arm segment length, and location, and outputs images showing the steps SpArcFire took to detect arm segments.

The Balrog package of Python simulation code is for use with real astronomical imaging data. Objects are simulated into a survey's images and measurement software is run over the simulated objects' images. Balrog allows the user to derive the mapping between what is actually measured and the input truth. The package uses GalSim (ascl:1402.009) for all object simulations; source extraction and measurement is performed by SExtractor (ascl:1010.064). Balrog facilitates the ease of running these codes en masse over many images, automating useful GalSim and SExtractor functionality, as well as filling in many bookkeeping steps along the way.

nimbus is a hierarchical Bayesian framework to infer the intrinsic luminosity parameters of kilonovae (KNe) associated with gravitational-wave (GW) events, based purely on non-detections. This framework makes use of GW 3-D distance information and electromagnetic upper limits from a given survey for multiple events, and self-consistently accounts for finite sky-coverage and probability of astrophysical origin.

Skymapper maps astronomical survey data from the celestial sphere onto 2D using a collection of matplotlib instructions. It facilitates interactive work as well as the creation of publication-quality plots with a python-based workflow many astronomers are accustomed to. The primary motivation is a truthful representation of samples and fields from the curved sky in planar figures, which becomes relevant when sizable portions of the sky are observed.

snmachine reads in photometric supernova light curves, extracts useful features from them, and subsequently performs supervised machine learning to classify supernovae based on their light curves. This python library is also flexible enough to easily extend to general transient classification.

The C code ReionYuga generates the Epoch of Reionization (EoR) neutral Hydrogen (HI) field (successively the redshifted 21-cm signal) within a cosmological simulation box using semi-numerical techniques. The code is based on excursion set formalism and uses a three parameter model. It is designed to work with PMN-body (ascl:2107.003) and FoF-Halo-finder (ascl:2107.004).

FoF-Halo-finder identifies the location and size of collapsed objects (halos) within a cosmological simulation box. These halos are the host for the luminous objects in the Universe. Written in C, it is based on the friends-of-friends (FoF) algorithm, and is designed to work with PMN-body (ascl:2107.003).

PMN-body computes the non-linear evolution of the cosmological matter density contrast. It is based on the Particle Mesh (PM) technique. Written in C, the code is parallelized for shared-memory machines using Open Multi-Processing (OpenMP).

ROA (Running Optimal Average) describes time series data. This model uses a Gaussian window function that moves through the data giving stronger weights to points close to the center of the Gaussian. Therefore, the width of the window function, delta, controls the flexibility of the model, with a small delta providing a very flexible model. The function also calculates the effective number of parameters, as a very flexible model will correspond to large number of parameters while a rigid model (low delta) has a low effective number of parameters. Knowing the effective number of parameters can be used to optimize the window width, e.g., using the Bayesian information criterion (BIC). An error envelope, which expands appropriately where there are gaps in the data, is also calculated for the model.

light-curve implements the extraction of numerous light curve features suitable for processing alert and archival data for the current ZTF and future Vera Rubin Observatory LSST photometric surveys. These high-performance irregular time series processing tools are written in Rust and Python.

GalaXimView (for Galaxies Simulations Viewer) is a python3+matplotlib tool designed to visualise simulations which use particles, providing notably a rotatable 3D view and corresponding projections in 2D, together with a way of navigating through snapshots of a simulation keeping the same projection.

iragnsep performs IR SED fits separated into AGN and galaxy contributions, and measures host galaxy properties free of AGN contamination. The advantage of iragnsep is that, in addition to fitting observed broadband photometric fluxes, it also incorporates IR spectra in the fits which, if available, improves the robustness of the galaxy-AGN separation. For the galaxy component, iragnsep uses a library of galaxy templates. In terms of the AGN contribution, if the input dataset is a mixture of spectral and photometric data, iragnsep uses a combination of power-laws for the AGN continuum, and some broad features for the silicate emission. If instead the dataset contains photometric data alone, the AGN contribution is accounted for by using a library of AGN templates. The advanced fitting techniques used by iragnsep combined with the powerful model comparison tests allows iragnsep to provide a statistically robust interpretation of IR SEDs in terms of AGN-galaxy contributions, even when the AGN contribution is highly diluted by the host galaxy emission.

Atmos contains two atmospheric models and scripts to couple them together. One atmospheric model calculates the profiles of chemical species, including both gaseous and aerosol phases, and the second model calculates the temperature profile. Because these profiles depend on each other - kinetic reaction rates are temperature-dependent and radiative transfer is subject to radiatively active gases - atmos alternates the running of these two models until both models have solutions consistent with the other one. While either of these models can be run with time-dependence, most applications of these models are to find steady-state solutions for the atmosphere that would be stable over long (geological/astronomical) time periods, given constant inputs to the atmosphere.

ehtplot creates publication quality, elegant, and consistent plots. Written for the Event Horizon Telescope (EHT) Collaboration, it provides a set of easy-to-use plotting functions for EHT and Very-Long-Baseline Interferometry (VLBI) specific figures. This includes plotting visibility and images for both synthetic and real data, adding uv-tracks to the plots, and adding the expected event horizon size to the plots, among other functions.

PORTA solves three-dimensional non-equilibrium radiative transfer problems with massively parallel computers. The code can be used for modeling the spectral line polarization produced by the scattering of anisotropic radiation and the Hanle and Zeeman effects assuming complete frequency redistribution, either using two-level or multilevel atomic models. The numerical method of solution used to find the self-consistent values of the atomic density matrix at each point of the model’s Cartesian grid is based on Jacobi iterative scheme and on a short-characteristics formal solver of the Stokes-vector transfer equation that uses monotonic Bézier interpolation. The code can also be used to compute the linear polarization of the continuum radiation caused by Rayleigh and Thomson scattering in 3D models of stellar atmospheres, and to solve the simpler 3D radiative transfer problem of unpolarized radiation in multilevel systems. PORTA accepts/produces HDF5 input/output and offers an advanced graphical user interface.

BiFFT uses Fourier transforms to implement the Dirac-Delta function that enforces a closed triangle of three k-vectors; this allows very fast calculations of the bispectrum. Once the C code associated with the package is compiled and the source folder directed to the location of the C code, the user can run the code using the python wrapper.The binning in each function has been tested over the course of many years and the user can use it out of the box without ever touching the underlying C code. However, the cylindrical bispectrum calculation is much more sensitive to sample variance; its default binning is quite coarse and might need adjusting (and testing) for some datasets.

CalPriorSNIa quickly computes the effective calibration prior on the absolute magnitude M_B of Type Ia supernovae that corresponds to a given determination of H₀.

The SNAD ZTF DR4 object viewer enables quick expert investigation of objects within the public Zwicky Transient Facility (ZTF) data releases. The viewer allows visualization of raw and folded light curves and metadata, as well as cross-match information with the General Catalog of Variable Stars, the International Variable Stars Index, the ATLAS Catalog of Variable Stars, the ZTF Catalog of Periodic Variable Stars, the Transient Name Server, the Open Astronomy Catalogs, the OGLE III Catalog of Variable Stars, the Simbad Astronomical Data Base, Gaia DR2 distances (Bailer-Jones+, 2018), and Vizier. The viewer is also available for ZTF DR2 and ZTF DR3.

ZWAD (ZTF anomaly detection pipeline) examines data and performs tailored feature extraction. The code then uses machine learning methods to searches for outliers, and identifies anomalies to be examined for validation by experts. Used with the SNAD ZTF data releases object viewer (ascl:2106.034), the infrastructure helps experts to form global views of specific scientifically interesting candidates.

DarkSirensStat statistically measures modified gravitational wave (GW) propagation and the Hubble parameter. The package implements a hierarchical Bayesian framework for constraining the Hubble parameter and modified GW propagation with dark sirens and galaxy catalogs. The package downloads the needed data; which include the GLADE galaxy catalog, O2 and O3 skymaps from the LVC official data releases, and O2 and O3 strain sensitivities. The default options are for running inference for H0 on the O3 BBH events, with flat prior between 20 and 140, mask completeness with 9 masks, interpolation between multiplicative and homogeneous completion, B-band luminosity weights, and a completeness threshold of 50%. The selection effects are computed with MC.

BiHalofit fits the matter bispectrum in the nonlinear regime calibrated by high-resolution cosmological N-body simulations of 41 cold dark matter models around the Planck 2015 best-fit parameters. The parameterization is similar to that in Halofit (ascl:1402.032). The simulation volume is sufficiently large to cover almost all measurable triangle bispectrum configurations in the universe, and the function is calibrated using one-loop perturbation theory at large scales. BiHaloFit predicts the weak-lensing bispectrum and will assist current and future weak-lensing surveys and cosmic microwave background lensing experiments.

DM_statistics calculates the free-electron power spectrum and the cosmological dispersion measure (DM) statistics (such as its mean and variance, angular power spectrum and correlation function). The default cosmological parameters are consistent with the Planck 2015 LambdaCDM model; the cosmological model can be easily changed by editing a few lines of the C code.

EMBERS provides a modular framework for radio telescopes and interferometric arrays such as the MWA, HERA, and the upcoming SKA-Low to accurately measure the all sky polarized beam responses of their antennas using weather and communication satellites. This tool enables astronomers and system engineers, all over the world, to characterize the in-situ antenna beam patterns of large arrays with ease.

FRBSTATS provides a user-friendly web interface to an open-access catalog of fast radio bursts (FRBs) published up to date, along with a highly accurate statistical overview of the observed events. The platform supports the retrieval of fundamental FRB data either directly through the FRBSTATS API, or in the form of a CSV/JSON-parsed database, while enabling the plotting of parameter distributions for a variety of visualizations. These features allow researchers to conduct more thorough population studies while narrowing down the list of astrophysical models describing the origins and emission mechanisms behind these sources. Lastly, the platform provides a visualization tool that illustrates associations between primary bursts and repeaters, complementing basic repeater information provided by the Transient Name Server.

MultiModeCode facilitates efficient Monte Carlo sampling of prior probabilities for inflationary model parameters and initial conditions and efficiently generates large sample-sets for inflation models with O(100) fields. The code numerically solves the equations of motion for the background and first-order perturbations of multi-field inflation models with canonical kinetic terms and arbitrary potentials, providing the adiabatic, isocurvature, and tensor power spectra at the end of inflation. For models with sum-separable potentials MultiModeCode also computes the slow-roll prediction via the δN formalism for easy model exploration and validation.

Katu evolves the interaction of particles (photons, protons, neutrons, leptons, pions and neutrinos) in plasma. The package comes with wrappers for emcee (ascl:1303.002) and pymultinest (ascl:1606.005) for Bayesian analysis, making the software applicable to blazars and able to extract relevant statistical information from their electromagnetic (and neutrino, if applicable) flux. The code is optimized for fast performance, and can be easily modified and extended.

ModeChord computes the primordial scalar and tensor power spectra for single field inflationary models. The code solves the inflationary mode equations numerically, avoiding the slow roll approximation. It provides an efficient and robust numerical evaluation of the inflationary perturbation spectrum, and allows the free parameters in the inflationary potential to be estimated. ModeChord also allows the estimation of reheating uncertainties once a potential has been specified.

The RedPipe collection of Python scripts performs optical photometric and spectroscopic data reduction. There are scripts on preprocessing, photometry, calibration, spectroscopy, analysis and plotting. The photometry and spectroscopy codes use pyraf (ascl:1207.011) and hence require an already existing installation of Image Reduction and Analysis Facility (IRAF, ascl:9911.002).

so_noise_models is the N(ell) noise curve projection code for the Simons Observatory. The code, written in pure Python, consists of several independent sub-modules, representing each version of the noise code. The usage of the models can vary substantially from version to version. The package also includes demo code that that demonstrates usage of the noise models, such as by producing noise curve plots, effective noise power spectra for SO LAT component-separated CMB T, E, B, and Compton-y maps, and lensing noise curves from SO LAT component-separated CMB T, E, B maps.

The 3D grid-based Monte Carlo code STaRS (Sejong Radiative Transfer through Raman and Rayleigh Scattering with atomic hydrogen) traces radiative transfer through Raman and Rayleigh scattering. This can be used to investigate line formation of Raman-scattered features in a thick neutral region illuminated by a strong far-UV emission source. Favorable conditions for Raman scattering with atomic hydrogen are easily met in symbiotic stars, young planetary nebulae, and active galactic nuclei.

aztekas solves hyperbolic partial differential equations in conservative form using High Resolution Shock-Capturing (HRSC) schemes. The code can solve the non-relativistic and relativistic hydrodynamic equations of motion (Euler equations) for a perfect fluid. The relativistic part can solve these equations on a background fixed metric, such as for Schwarzschild, Minkowski, Kerr-Schild, and others.

simple_reg_dem reconstructs differential emission measures (DEMs) in the solar corona. It overcomes issues, such as complexity, idiosyncratic output, convergence difficulty, and lack of speed, that exists in other methods. Initially written for extreme ultraviolet (EUV) data, the algorithm is notable for its simplicity, and is robust and extensible to any other wavelengths (e.g., X-rays) where the DEM treatment is valid. It is available in the SolarSoft (ascl:1208.013) package.

GLEMuR (Gpu-based Lagrangian mimEtic Magnetic Relaxation) is a finite difference Lagrangian code which uses mimetic differential operators and runs on Nvidia GPUs. Its main purpose is to study the relaxation of magnetic relaxation in environments of zero resistivity and viscosity; it preserves the magnetic flux and the topology of magnetic field lines. The use of mimetic operators for the spatial derivatives improve accuracy for high distortions of the grid, and the final state of the simulation approximates a force-free state with a significantly higher accuracy. Note, however, that GLEMuR is not a general purpose equation solver and the full magnetohydrodynamics equations are not implemented.

picca fits continua of forests, computes correlation functions (1D and 3D) and power-spectra (1D), computes covariance matrices, and fits models for the correlation functions. This set of tools is used for the analysis of the Lyman-alpha forest sample from the extended Baryon Oscillation Spectroscopic Survey (eBOSS) and the Dark Energy Spectroscopic Instrument (DESI).

redvsblue measures a precise redshift given a broad redshift prior. For each emission line or the full spectrum, the software runs a coarse chi2 scan as a function of redshift, using the input PCA+broadband Legendre polynomials, and finds three local minima, and does a finer chi2 scan in each minima. It then defines the global PCA redshift (ZPCA) from the best minimum of the three; ZPCA is a redshift estimator biased toward the computation of the PCA. The redshift of the line (ZLINE) is defined from the maximum of the best-fit model of the line. ZLINE is a redshift estimator un-biased toward the velocity of the line, but can be biased with respect to the cosmological redshift. The output is a FITS file, with one HDU per redshift type.

QuasarNET is a deep convolutional neural network that performs classification and redshift estimation of astrophysical spectra with human-expert accuracy. It is trained on data of low signal-to-noise and medium resolution, typical of current and future astrophysical surveys, and could be easily applied to classify spectra from current and upcoming surveys such as eBOSS, DESI and 4MOST.

The ATES hydrodynamics code computes the temperature, density, velocity and ionization fraction profiles of highly irradiated planetary atmospheres, along with the current, steady-state mass loss rate. ATES solves the one-dimensional Euler, mass and energy conservation equations in
radial coordinates through a finite-volume scheme. The hydrodynamics module is paired with a photoionization equilibrium solver that includes cooling via bremsstrahlung, recombination and collisional excitation/ionization for the case of an atmosphere of primordial composition (i.e., pure atomic hydrogen-helium), while also accounting for advection of the different ion species.

Lemon solves the radiative transfer (RT) processes that contain scattering. These processes are described by differentio-integral equations with given initial or boundary conditions; Lemon solves these differentio-integral equations, which can be converted into the second kind integral equations of Fredholm. The code then obtains the Neumman solution (a series that consists of infinite terms of multiple integrals) from the Fredholm integral equation, and uses the Monte Carlo (MC) method to evaluate these integrals. Lemon is written in Fortran; IDL programs are included for plotting the results.

The Kadath library implements spectral methods in the context of theoretical physics. It is fully parallel; a sequential version can be installed. The library is written in C++, and solves a wide variety of problems. Several coordinates systems are implemented and additional geometries can be easily encoded. Partial differential equations of various types are discretized by means of spectral methods. The resulting system is solved using a Newton-Raphson iteration, allowing KADATH to deal with strongly non-linear situations. An optimized version of Kadath is available that improves memory management (reducing the number of uses of new and delete), inlines several member functions, and provides better management of the accessors for the arrays.

StarcNet (STAR Cluster classification NETwork) classifies star clusters from galaxy images taken by the Hubble Space Telescope (HST); it uses a convolutional neural network (CNN) trained to classify five-band galaxy images into four morphological classes. Written in PyTorch, StarcNet runs using mosaics (.fits files with the galaxy photometric information) and catalogs (.tab files with object coordinates), and includes the option to also download the galaxy mosaics from a single .tar.gz file per galaxy (as from the Legacy ExtraGalactic UV Survey).

MakeCloud makes turbulent giant molecular cloud (GMC) initial conditions for GIZMO (ascl:1410.003). It generates turbulent velocity fields on the fly and stores that data in a user-specified path for efficiency. The code is flexible, allowing the user control through various parameters, including the radius of the cloud, number of gas particles, type of initial turbulent velocity (Gaussian or full), and magnetic energy as a fraction of the binding energy, among other options. With an additional file, it can also create glassy initial conditions.

The Maneage (Managing data lineage; ending pronounced like "lineage") framework produces fully reproducible computational research. It provides full control on building the necessary software environment from a low-level C compiler, the shell and LaTeX, all the way up to the high-level science software in languages such as Python without a third-party package manager. Once the software environment is built, adding analysis steps is as easy as defining "Make" rules to allow parallelized operations, and not repeating operations that do not need to be recreated. Make provides control over data provenance. A Maneage'd project also contains the narrative description of the project in LaTeX, which helps prepare the research for publication. All results from the analysis are passed into the report through LaTeX macros, allowing immediate dynamic updates to the PDF paper when any part of the analysis has changed. All information is stored in plain text and is version-controlled in Git. Maneage itself is actually a Git branch; new projects start by defining a new Git branch over it and customizing it for a new project. Through Git merging of branches, it is possible to import infrastructure updates to projects.

baofit analyzes cosmological correlation functions to estimate parameters related to baryon acoustic oscillations and redshift-space distortions. It has primarily been used to analyze Lyman-alpha forest autocorrelations and cross correlations with the quasar number density in BOSS data. Fit models are fully three-dimensional and include flexible treatments of redshift-space distortions, anisotropic non-linear broadening, and broadband distortions.

simqso generates mock quasar spectra and photometry. Simulated quasar spectra are built from a series of components. Common quasar models are built-in, such as a broken power-law continuum model and Gaussian emission line templates; however, the code allows user-defined features to be included. Mock spectra are generated at arbitrary resolution and can be used to produce broadband photometry representative of a number of surveys.

CoMover determines the probability that two stars are co-moving and thus gravitationally bound. It uses the sky position, proper motion, parallax and optionally the heliocentric radial velocity of a host star (with their respective measurement errors), and compares it to the observables of a potential companion (with their respective measurement errors). The sky position and proper motion of the potential companion star are required, and its heliocentric radial velocity and parallax are facultative inputs to refine its co-moving probability.

If all kinematic observables of the host star are provided, a single spatial-kinematic model is built, consisting of a single 6-dimensional multivariate Gaussian in Galactic coordinates (XYZ) and space velocities (UVW). The observables of the potential companion are then compared to this model and a given field-stars model with Bayes' theorem by marginalizing over any missing kinematic observables of the companion star with analytical integral solutions. The field stars are modeled using a 10-component multivariate Gaussian, accurate for stars within a few hundred parsecs of the Sun. In the case where a heliocentric radial velocity is missing for the host star, the single host-star multivariate Gaussian model is replaced with a series of host star models and numerically marginalized over by taking the numerical sum of the host-star model probabilities.

Pyshellspec models binary systems with circumstellar matter (e.g. accretion disk, jet, shell), computes the interferometric observables |V2|, arg T3, |T3|, |dV|, and arg dV, and performs comparisons of light curves, spectro-interferometry, spectra, and SED with observations, and both global and local optimization of system parameters. The code solves the inverse problem of finding the stellar and orbital parameters of the stars and circumstellar medium. Pyshellspec is based on the long-characteristic LTE radiation transfer code Shellspec (ascl:1108.017).

Marvin searches, accesses, and visualizes data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. Written in Python, it provides tools for easy efficient interaction with the MaNGA data via local files, files retrieved from the Science Archive Server, or data directly grabbed from the database. The tools come mainly in the form of convenience functions and classes for interacting with the data. Also available is a web app, Marvin-web, offers an easily accessible interface for searching the MaNGA data and visual exploration of individual MaNGA galaxies or of the entire sample, and a powerful query functionality that uses the API to query the MaNGA databases and return the search results to your python session. Marvin-API is the critical link that allows Marvin-tools and Marvin-web to interact with the databases, which enables users to harness the statistical power of the MaNGA data set.

crowdsource removes a rough sky (the median), find the brighter peaks and fits these sources, computes centroids, and then computes an improved PSF. With this model of the image, the code then iteratively subtracts it and recomputes the median to get a better sky estimate, finds fainter peaks, and calculates a better PSF. crowdsource performs at least four iterations, evaluates the results, and continues until certain thresholds are met. Once the iterative passes are complete, it makes one last pass. If no sources are detected and positions do not vary, it performs photometry for the existing list of stellar positions.

PyDoppler is a python-based wrapper for the Spruit Doppler tomography software dopmap (ascl:2106.002). PyDoppler is designed to study time-resolved spectroscopic datasets of accreting compact binaries. This code can produce a trail spectra of a dataset and create Doppler tomography maps. It is intended to be a light-weight code for single emission line datasets.

dopmap constructs Doppler maps from the orbital variation of line profiles of (mass transferring) binaries. It uses an algorithm related to Richardson-Lucy iteration and includes an IDL-based set of routines for manipulating and plotting the input and output data.

KOBE (Kepler Observes Bern Exoplanets) adds the geometrical limitations and the physical detection biases of the transit method to a given population of theoretical planets. In addition, it also adds the completeness and reliability of a transit survey.

PFITS performs data reduction of spectra, including dark removal and flat fielding; this software was a standard 1983 Reticon reduction package available at the University of Texas. It was based on the plotting program PCOSY by Gary Ferland, and in 1985 was updated by Andrew McWilliam.

Kepler's Goat Herd solves Kepler's equation using contour integration to solve the "geometric goat problem". The C++ code implements a variety of solution: 1.) Newton-Raphson: The quadratic Newton-Raphson root finder; 2.) Danby: The quartic root; 3.) Series: An elliptical series method; and 4.) Contour: A new method based on contour integration. Given an array of mean anomalies, an eccentricity and a desired precision, the code estimates the eccentric anomaly using each method. The accuracy of each approach is increased until the desired precision is reached, and timing is performed using the C++ chrono package.

The PHANGS-ALMA pipeline process data from radio interferometer observations. It uses CASA (ascl:1107.013), AstroPy (ascl:1304.002), and other affiliated packages to process data from calibrated visibilities to science-ready spectral cubes and maps. The PHANGS-ALMA pipeline offers a flexible alternative to the scriptForImaging script distributed by ALMA. The pipeline runs in two separate software environments: CASA 5.6 or 5.7 (staging, imaging and post-processing) and Python 3.6 or later (derived products) with modern versions of several packages.

RandomQuintessence integrates the Klein-Gordon and Friedmann equations for quintessence models with random initial conditions and functional forms for the potential. Quintessence models generically impose non-trivial structure on observables like the equation of state of dark energy. There are three main modules; montecarlo_nompi.py sets initial conditions, loops over a bunch of randomly-initialised models, integrates the equations, and then analyses and saves the resulting solutions for each model. Models are defined in potentials.py; each model corresponds to an object that defines the functional form of the potential, various model parameters, and functions to randomly draw those parameters. All of the equation-solving code and methods to analyze the solution are kept in solve.py under the base class DEModel(). Other files available analyze and plot the data in a variety of ways.

ClaRAN (Classifying Radio sources Automatically with Neural networks) classifies radio source morphology based upon the Faster Region-based Convolutional Neutral Network (Faster R-CNN). It is capable of associating discrete and extended components of radio sources in an automated fashion. ClaRAN demonstrates the feasibility of applying deep learning methods for cross-matching complex radio sources of multiple components with infrared maps. The promising results from ClaRAN have implications for the further development of efficient cross-wavelength source identification, matching, and morphology classifications for future radio surveys.

Pyrat Bay computes radiative-transfer spectra and fits exoplanet atmospheric properties, and is an efficient, user-friendly Python tool. The package offers transmission or emission spectra of exoplanet transit or eclipses respectively and forward-model or retrieval calculations. The radiative-transfer includes opacity sources from line-by-line molecular absorption, collision-induced absorption, Rayleigh scattering absorption, and more, including Gray aerosol opacities. Pyrat Bay's Bayesian (MCMC) posterior sampling of atmospheric parameters includes molecular abundances, temperature profile, pressure-radius, and Rayleigh and cloud properties.

CUDAHM accelerates Bayesian inference of Hierarchical Models using Markov Chain Monte Carlo by constructing a Metropolis-within-Gibbs MCMC sampler for a three-level hierarchical model, requiring the user to supply only a minimimal amount of CUDA code. CUDAHM assumes that a set of measurements are available for a sample of objects, and that these measurements are related to an unobserved set of characteristics for each object. For example, the measurements could be the spectral energy distributions of a sample of galaxies, and the unknown characteristics could be the physical quantities of the galaxies, such as mass, distance, or age. The measured spectral energy distributions depend on the unknown physical quantities, which enables one to derive their values from the measurements. The characteristics are also assumed to be independently and identically sampled from a parent population with unknown parameters (e.g., a Normal distribution with unknown mean and variance). CUDAHM enables one to simultaneously sample the values of the characteristics and the parameters of their parent population from their joint posterior probability distribution.

PyTorchDIA is a Difference Image Analysis tool. It is built around the PyTorch machine learning framework and uses automatic differentiation and (optional) GPU support to perform fast optimizations of image models. The code offers quick results and is scalable and flexible.

encore (Efficient N-point Correlator Estimation) estimates the isotropic NPCF multipoles for an arbitrary survey geometry in O(N²) time, with optional GPU support. The code features support for the isotropic 2PCF, 3PCF, 4PCF, 5PCF and 6PCF, with the option to subtract the Gaussian 4PCF contributions at the estimator level. For the 4PCF, 5PCF and 6PCF algorithms, the runtime is dominated by sorting the spherical harmonics into bins, which has complexity O(N_galaxy x N_bins³ x N_ell⁵) [4PCF], O(N_galaxy x N_bins⁴ x N_ell⁸) [5PCF] or O(N_galaxy x N_bins⁵ x N_ell¹¹) [6PCF]. The higher-point functions are slow to compute unless N_bins and N_ell are small.

SISPO (Space Imaging Simulator for Proximity Operations) simulates trajectories, light parameters, and camera intrinsic parameters for small solar system body fly-by and terrestrial planet surface missions. The software provides realistic surface rendering and realistic dust- and gas-environment optical models for comets and active asteroids and also simulates common image aberrations such as simple geometric distortions and tangential astigmatism. SISPO uses Blender and its Cycles rendering engine, which provides physically based rendering capabilities and procedural micropolygon displacement texture generation.

orvara (Orbits from Radial Velocity, Absolute, and/or Relative Astrometry) fits orbits of bright stars and their faint companions (exoplanets, brown dwarfs, white dwarfs, and low-mass stars). It can use any combination of radial velocity, relative astrometry, and absolute astrometry data and offers a variety of plots from the orbital fit, such as the radial velocity orbit over an extended time baseline, position angle between two companions, and a density plot of the predicted position at a chosen epoch. orvara can also check convergence of fitted parameters in the HDU1 extension, save the results from the fitted and inferred parameters from the HDU1 extension, and plot the results of a three-body or multiple-body fit.

BlackBOX performs standard CCD image reduction tasks on multiple images from the BlackGEM and MeerLICHT telescopes. It uses the satdet module of ASCtools (ascl:2011.024) and Astro-SCRAPPY (ascl:1907.032). BlackBOX simultaneously uses multi-processing and multi-threading and feeds the reduced images to ZOGY (ascl:2105.010) to ultimately perform optimal image subtraction and detect transient sources.

ZOGY performs optimal image subtraction; the code is designed specifically for the MeerLICHT and BlackGEM pipelines, but should also be useful to apply to images of other telescopes. The module accepts a new and a reference FITS image, runs SExtractor (ascl:1010.064) on them, and finds their WCS solution using Astrometry.net (ascl:1208.001). ZOGY then uses PSFex (ascl:1301.001) to infer the position-dependent PSFs of the images and SWarp (ascl:1010.068) to map the reference image to the new image and performs optimal image subtraction. This produces the subtracted image, the significance image, the corrected significance image, and the PSF photometry image and associated error image. The inferred PSFs are also used to extract optimal photometry of all sources detected by SExtractor.

MeerCRAB (MeerLICHT Classification of Real and Bogus Transients using Deep Learning) filters out false detections of transients from true astrophysical sources in the transient detection pipeline of the MeerLICHT telescope. It uses a deep learning model based on Convolutional Neural Network.

MCALF (Multi-Component Atmospheric Line Fitting) accurately constrains velocity information from spectral imaging observations using machine learning techniques. It is useful for solar physicists trying to extract line-of-sight (LOS) Doppler velocity information from spectral imaging observations (Stokes I measurements) of the Sun. A toolkit is provided that can be used to define a spectral model optimized for a particular dataset. MCALF is particularly suited for extracting velocity information from spectral imaging observations where the individual spectra can contain multiple spectral components. Such multiple components are typically present when active solar phenomenon occur within an isolated region of the solar disk. Spectra within such a region will often have a large emission component superimposed on top of the underlying absorption spectral profile from the quiescent solar atmosphere.

SpheCow explores the structure and dynamics of any spherical model for galaxies and dark matter haloes. The lightweight and flexible code automatically calculates the dynamical properties, assuming an isotropic or Osipkov-Merritt anisotropic orbital structure, of any model with either an analytical density profile or an analytical surface density profile as a starting point. SpheCow contains readily usable implementations for many standard models, including the Plummer, Hernquist, NFW, Einasto, Sérsic and Nuker models. The code is easily extendable, allowing new models to be added in a straightforward way. The code is publicly available as a set of C++ routines and as a Python module.

The Sequencer reveals the main sequence in a dataset if one exists. To do so, it reorders objects within a set to produce the most elongated manifold describing their similarities which are measured in a multi-scale manner and using a collection of metrics. To be generic, it combines information from four different metrics: the Euclidean Distance, the Kullback-Leibler Divergence, the Monge-Wasserstein or Earth Mover Distance, and the Energy Distance. It considers different scales of the data by dividing each object in the input data into separate parts (chunks), and estimating pair-wise similarities between the chunks. It then aggregates the information in each of the chunks into a single estimator for each metric+scale.

COMPAS (Compact Object Mergers: Population Astrophysics & Statistics) draws properties for a binary star system from a set of initial distributions and evolves it from zero-age main sequence to the end of its life as two compact remnants. Evolution prescriptions and model parameters are easily adjustable in the software. COMPAS has been used for inference from observations of gravitational-wave mergers, Galactic neutron stars, X-ray binaries, and luminous red novae.

TesseRACt computes concentrations of simulated dark matter halos from volume information for particles generated using Voronoi tesselation. This technique is advantageous as it is non-parametric, does not assume spherical symmetry, and allows for the presence of substructure. TesseRACt accepts data in a number of formats, including Gadget-2 (ascl:0003.001), Gasoline (ascl:1710.019), and ASCII, and computes concentrations using particles volumes, traditional fitting to an NFW profile, and non-parametric techniques that assume spherical symmetry.

ATARRI is a graphical user interface for downloading TESS Full Frame Images (FFIs) and displaying properties of the lightcurves of selected objects. Preliminary analysis is performed assuming the object is an RR Lyrae variable. The raw lightcurve, a Lomb-Scargle analysis (both full and pre-whitened), and a folded lightcurve are presented to the user along with options to select the type of RR Lyrae and data quality flags for output.

PDM2 (Phase Dispersion Minimization) ddetermines periodic components of data sets with erratic time intervals, poor coverage, non-sine-wave curve shape, and/or large noise components. Essentially a least-squares fitting technique, the fit is relative to the mean curve as defined by the means of each bin; the code simultaneously obtains the best least-squares light curve and the best period. PDM2 allows an arbitrary degree of smoothing and provides improved curve fits, suppressed subharmonics, and beta function statistics.

The Black Hole Perturbation Toolkit models gravitational radiation from small mass-ratio binaries as well as from the ringdown of black holes. The former are key sources for the future space-based gravitational wave detector LISA. BHPToolkit brings together core elements of multiple scattered black hole perturbation theory codes into a Toolkit that can be used by all; different tools can be installed individually by users depending on need and interest.

Phase Dispersion Minimization (PDM) is a periodical signal detection method, and it is originally implemented by Stellingwerf with C (https://www.stellingwerf.com/rfs-bin/index.cgi?action=PageView&id=34). With the help of Cython, Py-PDM is much faster than other Python implementations.

Posidonius is a N-body code based on the tidal model used in Mercury-T (ascl:1511.020). It uses the REBOUND (ascl:1110.016) symplectic integrator WHFast to compute the evolution of positions and velocities, which is also combined with a midpoint integrator to calculate the spin evolution in a consistent way. As Mercury-T, Posidonius takes into account tidal forces, rotational-flattening effects and general relativity corrections. It also includes different evolution models for FGKML stars and gaseous planets. The N-Body code is written in Rust; a Python package is provided to easily define simulation cases in JSON format, which is readable by the Posidonius integrator.

Lofti_gaia fits orbital parameters for one wide stellar binary relative to the other, when both objects are resolved in Gaia DR2. It takes as input only the Gaia DR2 source id of the two components, and their masses. It retrieves the relevant parameters from the Gaia archive, computes observational constraints for them, and fits orbital parameters to those measurements. It assumes the two components are bound in an elliptical orbit.

TES models the evolution of exoplanet systems. This n-body integration package comes in two parts, the C++ TES source code, and the Python-based experiment manager for running experiments and plotting the results. The experiment manager, used as the interface to TES, handles temporary data storage and allows for experiment results to be saved and then loaded later on for plotting. The experiment manager can automatically use multiple threads to run independent experiments in parallel using the mpi4py package. The experiment manager is specifically designed to enable HPC to be performed as easily as possible.

globalemu emulates the Global or sky averaged 21-cm signal and the associated neutral fraction history. The code can train a network on your own Global 21-cm signal or neutral fraction simulations using the built-in globalemu pre-processing techniques. It also features a GUI that can be invoked from the command line and used to explore how the structure of the Global 21-cm signal varies with the values of the astrophysical inputs.

linemake generates formatted and curated atomic and molecular line lists suitable for spectral synthesis work. It is lightweight and easy-to-use. The code requires that the requested beginning and ending wavelengths not bridge the divide between two files of atomic line data; in such cases, run the code twice, once on either side of the divide, to generate the desired lists.

The Skye framework develops and prototypes new EOS physics; it is not tied to a specific set of physics choices and can be extended for new effects by writing new terms in the free energy. It takes into account the effects of positrons, relativity, electron degeneracy, and non-linear mixing effects and more, and determines the point of Coulomb crystallization in a self-consistent manner. It is available in the MESA (ascl:1010.083) EOS module and as a standalone package.

SpaceHub uses unique algorithms for fast precise and accurate computations for few-body problems ranging from interacting black holes to planetary dynamics. This few-body gravity integration toolkit can treat black hole dynamics with extreme mass ratios, extreme eccentricities and very close encounters. SpaceHub offers a regularized Radau integrator with round off error control down to 64 bits floating point machine precision and can handle extremely eccentric orbits and close approaches in long-term integrations.

GAMMA models relativistic hydrodynamics and non-thermal emission on a moving mesh. It uses an arbitrary Lagrangian-Eulerian approach only in the dominant direction of fluid motion to avoid mesh entanglement and associated computational costs. Shock detection, particle injection and local calculation of their evolution including radiative cooling are done at runtime. The package is modular; though it was designed with GRB physics applications in mind, new solvers and geometries can be implemented easily, making GAMMA suitable for a wide range of applications.

PyBird evaluates the multipoles of the power spectrum of biased tracers in redshift space. In general, PyBird can evaluate the power spectrum of matter or biased tracers in real or redshift space. The code uses FFTLog (ascl:1512.017) to evaluate the one-loop power spectrum and the IR resummation. PyBird is designed for a fast evaluation of the power spectra, and can be easily inserted in a data analysis pipeline. It is a standalone tool whose input is the linear matter power spectrum which can be obtained from any Boltzmann code, such as CAMB (ascl:1102.026) or CLASS (ascl:1106.020). The Pybird output can be used in a likelihood code which can be part of the routine of a standard MCMC sampler. The design is modular and concise, such that parts of the code can be easily adapted to other case uses (e.g., power spectrum at two loops or bispectrum). PyBird can evaluate the power spectrum either given one set of EFT parameters, or independently of the EFT parameters. If the former option is faster, the latter is useful for subsampling or partial marginalization over the EFT parameters, or to Taylor expand around a fiducial cosmology for efficient parameter exploration.

RadioFisher is a Fisher forecasting code for cosmology with intensity maps of the redshifted 21cm emission line of neutral hydrogen. It uses CAMB (ascl:1102.026) to produce a high-resolution P(k) for the fiducial cosmology when the code is first run and caches the results, making subsequent runs faster and more efficient. It includes specifications for a large number of experiments, as well as survey parameters and the fiducial cosmological parameters, and can run a forecast for a galaxy redshift survey rather than an IM survey. RadioFisher also contains a number of options for plotting results.

cmblensplus reconstructs lensing potential, cosmic bi-refringence, and patchy reionization from cosmic microwave background anisotropies (CMB) in full and flat sky. This Fortran wrapper for Python also includes modules for delensing and bi-spectrum calculations. cmblensplus contains a module of basic routines such as analytic calculation of delensed B-mode spectrum and lensing bispectrum. Two additional main modules are for curved sky and flat sky analyses, and measure lensing, bi-refringence, patchy tau, bias-hardening, bi-spectrum, delensing and analytic reconstruction normalization. The package also contains simple Python utility and demonstration scripts. cmblensplus uses FFTW (ascl:1201.015), HEALPix (ascl:1107.018), LAPACK (ascl:2104.020), CFITSIO (ascl:1010.001), and LensPix (ascl:1102.025).

LAPACK provides routines for solving systems of simultaneous linear equations, least-squares solutions of linear systems of equations, eigenvalue problems, and singular value problems. The associated matrix factorizations (LU, Cholesky, QR, SVD, Schur, generalized Schur) are also provided, as are related computations such as reordering of the Schur factorizations and estimating condition numbers. Dense and banded matrices are handled, but not general sparse matrices. In all areas, similar functionality is provided for real and complex matrices, in both single and double precision. The list of LAPACK Contributors is available online.

SpectRes efficiently resamples spectra and their associated uncertainties onto an arbitrary wavelength grid. The Python function works with any grid of wavelength values, including non-uniform sampling, and preserves the integrated flux. This may be of use for binning data to increase the signal to noise ratio, obtaining synthetic photometry, or resampling model spectra to match the sampling of observational data.

GGchem is a fast thermo-chemical equilibrium code with or without equilibrium condensation down to 100K. It can handle up to 40 elements (H, ..., Zr, and W), up to 1155 molecules, and up to 200 condensates (solids and liquids) from NIST-JANAF and SUPCRTBL. It offers a customized selection of elements, molecules, and condensates. The Fortran-90 code is very fast, and has a stable iterative solution scheme based on Newton-Raphson.

Bagpipes generates realistic model galaxy spectra and fits these to spectroscopic and photometric observations.

Skyoffset makes wide-field mosaics of FITS images. Principal features of Skyoffset are the ability to produce a mosaic with a continuous background level by solving for sky offsets that minimize the intensity differences between overlapping images, and its handling of hierarchies, making it ideal for optimizing backgrounds in large mosaics made with array cameras (such as CFHT’s MegaCam and WIRCam). Skyoffset uses MongoDB in conjunction with Mo’Astro (ascl:2104.012) to store metadata about each mosaic and SWarp (ascl:1010.068) to handle image combination and propagate uncertainty maps. Skyoffset can be integrated into Python pipelines and offers a convenient API and metadata storage in MongoDB. It was developed originally for the Andromeda Optical and Infrared Disk Survey (ANDROIDS).

dense_basis implements the Dense Basis method tailored to SED fitting, in particular, the task of recovering accurate star formation history (SFH) information from galaxy spectral energy distributions (SEDs). The code's original use-case was simultaneously fitting specific large catalogs of galaxies; it is adapted to a general purpose SED fitting code, and acts as a module to compress and decompress SFHs and other time-series.

SSSpaNG is a data-driven Gaussian Process model of the spectra of APOGEE red clump stars, whose parameters are inferred using Gibbs sampling. By pooling information between stars to infer their covariance it permits clear identification of the correlations between spectral pixels. Harnessing this correlation structure, a complete spectrum for each red clump star can be inferred, inpainting missing regions and de-noising by a factor of at least 2-3 for low-signal-to-noise stars.

pfits reads, manipulates and processes PSRFITS format search- and fold-mode pulsar astronomy data files. It summerizes the header information in a PSRFITS file, reproduces some of fv's (ascl:1205.005) functionality, and allows the user to obtain detailed information about the file. It can determine whether the data is search mode or fold mode and plot the profile, color scale image, frequency time, sum in frequency, and 4-pol data, as appropriate. pfits can also read in a search mode file, dedisperses, and frequency-sums (if requested), and offers an option to output multiple dispersed data files, among other tasks.

Mo’Astro is a MongoDB framework for observational astronomy pipelines. Mo'Astro sets up a MongoDB collection of a survey's image set, keeping FITS metadata readily available, and providing a place in the reduction pipeline to persist metadata. Mo’Astro also provides facilities for batch processing images with the Astromatic tool suite, and for hosting a local 2MASS star catalog with spatial-search built-in.

FreeTure monitors images from GigE all-sky cameras to detect and record falling stars and fireball. Originally, it was developed for the FRIPON (Fireball Recovery and InterPlanetary Observation Network) project, which sought to cover all of France with 100 fish eyes cameras, but can be used by any station that has a GigE camera.

Ulula is an ultra-lightweight 2D hydro code for teaching purposes. The code is written in pure python and is designed to be as short and easy to understand as possible, while not compromising on performance. The latter is achieved with a simple Godunov solver and by using numpy for all array operations.

Optool computes dust opacities and scattering matrices, for specific grain sizes or averaged over size distributions. It is derived from OpacityTool (ascl:2104.009) and implements the Distribution of Hollow Spheres (DHS) statistical method to approximate irregular and low porosity grains. Mie theory is available as a limiting case of DHS. It also implements the Tazaki Modified Mean Field Theory (MMF) to treat fractal and highly porous aggregates. The refractive index data for many astronomically relevant materials are compiled into the code, and external refractive index data can be used as well. A compact and intuitive command line interface makes it easy to construct complex particles on the fly. Available output formats are ASCII and FITS, including files directly readable by RADMC-3D (ascl:1202.015). A python interface to the FORTRAN program is included.

OpacityTool computes dust opacities for disc modelling; it includes a number of robust facts obtained from observations and theory and goes beyond astronomical silicates. It provides output files with κext(λ),κabs(λ),κsca(λ) as a function of wavelength λ, and the 6 scattering matrix elements for randomly oriented particles, F11(λ,θ), F12(λ,θ), F22(λ,θ), F33(λ, θ), F34(λ, θ), F44(λ, θ) as functions of wavelength and scattering angle θ.

This code is superseded by optool (ascl:2104.010).

The NASA Langley Fu-Liou radiative transfer code (also known as Ed4 LaRC Fu-Liou) computes broadband solar shortwave and thermal long wave profiles of down-welling and up-welling flux accounting for gas absorption by H2O, CO2, O3, O2, CH4, N2O and CFCs and absorption and scattering by clouds and aerosols. Longwave has options of a four-stream or 2/4 stream solver, while shortwave has options for two-stream, four-stream or Gamma weighted two-stream (GWTSA) which treats the inhomogeniety of cloud optical depth. A delta-Eddington approximation is used to treat the forward scattering peak. Water cloud properties are based on Mie calculations and ice cloud properties or the ice particle aspect ratio. Aerosol properties are given for 25 types.

EPIC5 computes positions, velocities and densities along closed orbits of interstellar matter, including frictional forces, in a galaxy with an arbitrary perturbing potential. Radial velocities are given for chosen lines of sight. These are analytic gas orbits in an arbitrary rotating galactic potential using the linear epicyclic approximation

RJObject provides a general approach to trans-dimensional Bayesian inference problems, using trans-dimensional MCMC embedded within a Nested Sampling algorithm. This allows exploration of the posterior distribution and calculattion of the marginal likelihood (summed over N) even if the problem contains a phase transition or other difficult features such as multimodality.

CTR (Coronal Temperature Reconstruction) reconstructs differential emission measures (DEMs) in the solar corona. Written in IDL, the code guarantees positivity of the recovered DEM, enforces an explicit smoothness constraint, returns a featureless (flat) solution in the absence of information, and converges quickly. The algorithm is robust and can be extended to other wavelengths where the DEM treatment is valid.

Spectractor extracts spectra from slitless spectrophotometric images and measures the atmospheric transmission on the line of sight if standard stars are targeted. It has been optimized on CTIO images but can be configured to analyze any kind of slitless data that contains the order 0 and the order 1 of a spectrum. In particular, it can be used to estimate the atmospheric transmission of the Vera Rubin Observatory site using the dedicated Auxiliary Telescope.

Hilal-Obs authenticates lunar crescent first visibility reports. The code, written in Python, uses PyEphem (ascl:1112.014) for astrometrics, and takes into account all the factors that affect lunar crescent visibility, including atmospheric extinction, observer physiology, sky and lunar brightness, contrast threshold, and the type of observation.

The HERA Librarian system keeps track of all the primary data products for the telescope at a given site. The Librarian supports large data volumes and automated data processing capabilities. A web-based application handles human user and automatic requests and interfaces with a backing database and data storage servers. The system supports the long-term data storage of all relevant telescope data, as well as staging data to individual users' directories for processing.

The hera_opm package provides a convenient and flexible framework for developing data analysis pipelines for operating on a sequence of input files. Though developed for application to the Hydrogen Epoch of Reionization Array (HERA), it is a general package that can be applied to any workflow designed to apply a series of analysis steps to any type of files. It is also portable, operating both on a diversity of computer clusters with batch submission systems and local machines.

CARTA (Cube Analysis and Rendering Tool for Astronomy) is a image visualization and analysis tool designed for the ALMA, VLA, SKA pathfinders, and the ngVLA. If offers catalog support, shared region analytics, profile smoothing, and spectral line query, and more. CARTA adopts a client-server architecture suitable for visualizing images with large file sizes (GB to TB) easily obtained from ALMA, VLA, or SKA pathfinder observations; computation and data storage are handled by remote enterprise-class servers or clusters with high performance storage, while processed products are sent to clients only for visualization with modern web features, such as GPU-accelerated rendering. This architecture also enables users to interact with the ALMA and VLA science archives by using CARTA as an interface. CARTA provides a desktop version and a server version. The former is suitable for single-user usage with a laptop, a desktop, or a remote server in the "remote" execution mode. The latter is suitable for institution-wide deployment to support multiple users with user authentication and additional server-side features.

DIAPHANE provides a common platform for application-independent radiation and neutrino transport in astrophysical simulations. The library contains radiation and neutrino transport algorithms for modeling galaxy formation, black hole formation, and planet formation, as well as supernova stellar explosions. DIAPHANE is written in C and C++, but as many hydrodynamic codes use Fortran, the library includes examples of how to interface the library from the Fortran codes SPHYNX (ascl:1709.001) and RAMSES (ascl:1011.007).

SparseBLS uses the Box-fitting Least Squares (BLS) algorithm to detect transiting exoplanets in photometric data. SparseBLS does not bin data into phase bins and does not use a phase grid. Because its detection efficiency does not depend on the transit phase, it is significantly faster than BLS for sparse data and is well-suited for large photometric surveys producing unevenly-sampled sparse light curves, such as Gaia.

astrofix is an astronomical image correction algorithm based on Gaussian Process Regression. It trains itself to apply the optimal interpolation kernel for each image, performing multiple times better than median replacement and interpolation with a fixed kernel.

Gallenspy uses the gravitational lensing effect (GLE) to reconstruct mass profiles in disc-like galaxies. The algorithm inverts the lens equation for gravitational potentials with spherical symmetry, in addition to the estimation in the position of the source, given the positions of the images produced by the lens. Gallenspy also computes critical and caustic curves and the Einstein ring.

PyPion reads in Silo (ascl:2103.025) data files from PION (ascl:2103.024) simulations and plots the data. This library works for 1D, 2D, and 3D data files and for any amount of nested-grid levels. The scripts contained in PyPion save the options entered into the command line when the python script is run, open the silo file and save all of the important header variables, open the directory in the silo (or vtk, or fits) file and save the requested variable data (eg. density, temp, etc.), and set up the plotting function and the figure.

Silo reads and writes a wide variety of scientific data to binary disk files. The files Silo produces and the data within them can be easily shared and exchanged between wholly independently developed applications running on disparate computing platforms. Consequently, Silo facilitates the development of general purpose tools for processing scientific data. One of the more popular tools that process Silo data files is the VisIt visualization tool (ascl:1103.007).

Silo supports gridless (point) meshes, structured meshes, unstructured-zoo and unstructured-arbitrary-polyhedral meshes, block structured AMR meshes, constructive solid geometry (CSG) meshes, piecewise-constant (e.g., zone-centered) and piecewise-linear (e.g. node-centered) variables defined on the node, edge, face or volume elements of meshes as well as the decomposition of meshes into arbitrary subset hierarchies including materials and mixing materials. In addition, Silo supports a wide variety of other useful objects to address various scientific computing application needs. Although the Silo library is a serial library, it has features that enable it to be applied quite effectively and scalable in parallel.

PION (PhotoIonization of Nebulae) is a grid-based fluid dynamics code for hydrodynamics and magnetohydrodynamics, including a ray-tracing module for calculating the attenuation of radiation from point sources of ionizing photons. It also has a module for coupling fluid dynamics and the radiation field to microphysical processes such as heating/cooling and ionization/recombination. PION models the evolution of HII regions, photoionized bubbles that form around hot stars, and has been extended to include stellar wind sources so that both wind bubbles and photoionized bubbles can be simulated at the same time. It is versatile enough to be extended to other applications.

DRAKE (Dark matter Relic Abundance beyond Kinetic Equilibrium) predicts the dark matter relic abundance in situations where the standard assumption of kinetic equilibrium during the freeze-out process may not be satisfied. The code comes with a set of three dedicated Boltzmann equation solvers that implement, respectively, the traditionally adopted equation for the dark matter number density, fluid-like equations that couple the evolution of number density and velocity dispersion, and a full numerical evolution of the phase-space distribution.

nestle is a pure Python implementation of nested sampling algorithms for evaluating Bayesian evidence. Nested sampling integrates posterior probability in order to compare models in Bayesian statistics. It is similar to Markov Chain Monte Carlo (MCMC) in that it generates samples that can be used to estimate the posterior probability distribution. Unlike MCMC, the nature of the sampling also allows one to calculate the integral of the distribution. It is also a pretty good method for robustly finding global maxima.

Carsus manages atomic datasets. It requires Chianti (ascl:9911.004), and can read data from a variety of sources and output them to file formats readable by radiative transfer codes such as TARDIS (ascl:1402.018).

ARTIS is a 3D radiative transfer code for Type Ia supernovae using the Monte Carlo method with indivisible energy packets. It incorporates polarization and virtual packets and non-LTE physics appropriate for the nebular phase of Type Ia supernovae.

SuperNu simulates time-dependent radiation transport in local thermodynamic equilibrium with matter. It applies the methods of Implicit Monte Carlo (IMC) and Discrete Diffusion Monte Carlo (DDMC) for static or homologously expanding spatial grids. The radiation field affects material temperature but does not affect the motion of the fluid. SuperNu may be applied to simulate radiation transport for supernovae with ejecta velocities that are not affected by radiation momentum. The physical opacity calculation includes elements from Hydrogen up to Cobalt. SuperNu is motivated by the ongoing research into the effect of variation in the structure of progenitor star explosions on observables: the brightness and shape of light curves and the temporal evolution of the spectra. Consequently, the code may be used to post-process data from hydrodynamic simulations. SuperNu does not include any capabilities or methods that allow for non-trivial hydrodynamics.

GalacticDNSMass performs Bayesian inference on Galactic double neutron stars (DNS) to investigate their mass distribution. Each DNS is comprised of two neutron stars (NS), a recycled NS and a non-recycled (slow) NS. It compares two hypotheses: A - recycled NS and non-recycled NS follow an identical mass distribution, and B - they are drawn from two distinct populations. Within each hypothesis it also explore three possible functional models: Gaussian, two-Gaussian (mixture model), and uniform mass distributions.

CRIME (Cosmological Realizations for Intensity Mapping Experiments) generates mock realizations of intensity mapping observations of the neutral hydrogen distribution. It contains three separate tools, GetHI, ForGet, and JoinT. GetHI generates realizations of the temperature fluctuations due to the 21cm emission of neutral hydrogen. Optionally it can also generate a realization of the point-source continuum emission (for a given population) by sampling the same density distribution, though using this feature greatly affects performance. ForGet generates realizations of the different galactic and extra-galactic foregrounds relevant for intensity mapping experiments using some external datasets (e.g. the Haslam 408 MHz map) stored in the "data"folder. JoinT is provided for convenience; it joins the temperature maps generated by GetHI and ForGet and includes several instrument-dependent effects (in an overly simplistic way).

The RAiSERed (Radio AGN in Semi-analytic Environments: Redshifts) code implements the RAiSE analytic model for Fanaroff-Riley type II sources, using a Bayesian prior for their host cosmological environments, to measure the redshift of active galactic nuclei lobes based on radio-frequency observations. The Python code provides a class for the user to store measured attributes for each radio source, and to which model derived redshift probability density functions are returned. Systematic uncertainties in the analytic model can be calibrated by specifying a subset of radio sources with spectroscopic redshifts. Functions are additionally provided to plot the redshift probability density functions and assess the success of the model calibration.

LPF (Live Pulse Finder) provides real-time automated analysis of the radio image data stream at multiple frequencies. The fully automated GPU-based machine-learning backed pipeline performs source detection, association, flux measurement and physical parameter inference. At the end of the pipeline, an alert of a significant detection of a transient event can be sent out and the data saved for further investigation.

QuickCBC is a robust end-to-end low-latency Bayesian parameter estimation algorithm for binary mergers. It reads in calibrated strain data, performs robust on-source spectral estimation, executes a rapid search for compact binary coalescence (CBC) signals, uses wavelet de-noising to subtract any glitches from the search residuals, produces low-latency sky maps and initial parameter estimates, followed by full Bayesian parameter estimation.

schNell computes basic map-level noise properties for generic networks of gravitational wave interferometers, primarily the noise power spectrum "N_ell", but this lightweight python module that can also be used for, for example, antenna patterns, overlap functions, and inverse variance maps, among other tasks. The code has three main classes; detectors contain information about each individual detector of the network, such as their positions, noise properties, and orientation. NoiseCorrelations describes the noise-level correlation between pairs of detectors, and the MapCalculators class combines a list of Detectors into a network (potentially together with a NoiseCorrelation object) and computes the corresponding map-level noise properties arising from their correlations.

AstroNet-Triage contains TensorFlow models and data processing code for identifying exoplanets in astrophysical light curves; this is the triage version of two TESS neural networks. For the vetting version, see AstroNet-Vetting (ascl:2103.011). The TensorFlow code downloads and pre-processes TESS data, builds different types of neural network classification models, trains and evaluates new models, and generates new predictions using a trained model. Utilities that operate on light curves are provided; these reading TESS data from .h5 files, and perform phase folding, splitting, binning, and other tasks. C++ implementations of some light curve utilities are also included.

AstroNet-Vetting identifies exoplanets in astrophysical light curves. This is the vetting version of two TESS neural networks; for the triage version, see AstroNet-Triage (ascl:2103.012). The package contains TensorFlow code that downloads and pre-processes TESS data, builds different types of neural network classification models, trains and evaluates a new model, and uses a trained model to generate new predictions. It includes utilities for operating on light curves, such as for reading TESS data from .h5 files, phase folding, splitting, and binning. In addition, C++ implementations of light curve utilities are also provided.

TransitFit fits exoplanetary transit light-curves for transmission spectroscopy studies. The code uses nested sampling for efficient and robust multi-epoch, multi-wavelength fitting of transit data obtained from one or more telescopes. TransitFit allows per-telescope detrending to be performed simultaneously with parameter fitting, including the use of user-supplied detrending alogorithms. Host limb darkening can be fitted either independently ("uncoupled") for each filter or combined ("coupled") using prior conditioning from the PHOENIX stellar atmosphere models. For this, TransitFit uses the Limb Darkening Toolkit (ascl:1510.003) together with filter profiles, including user-supplied filter profiles.

The cosmology code DarkEmulator calculates summary statistics of large scale structure constructed as a part of Dark Quest Project. The “dark_emulator” python package enables fast and accurate computations of halo clustering quantities. The code supports the halo mass function, halo-matter cross-correlation, and halo auto-correlation as a function of halo masses, redshift, separations and cosmological models.

The MRS (The MOS Reduction Software) suite reduces the spectra taken with the multi-object spectrograph spectra used as the focal plane instrument of RTT150 telescope in the TÜBİTAK National Observatory.

Simple program for planning and managing astronomical observations as observational diary or logs.

Pyedra performs asteroid phase curve fitting. From a simple table containing the asteroid MPC number, phase angle and reduced magnitude, Pyedra estimates the parameters of the phase function using the least squares method. The user can choose from three different models for the phase curve fit: H-G model, H-G1-G2 model and the Shevchenko model. The output in all cases is a table containing the adjusted parameters and their corresponding errors. This package allows carrying out phase function analysis for a few asteroids as well as to process large volumes of data such as those released by current large surveys.

TFF derives the Fourier decomposition of period-folded RR Lyrae light curves with gaps. The method can be used for the same purpose on any other types of variables, assuming that the the template database is changed to the proper type of variables.

Ggm contains useful utilities for Gaussian gradient filtering of astronomical FITS images. It applies the Gaussian gradient magnitude filter to an input fits image, using a particular scale, sigma, in pixels. ggm cosmetically hides point sources in fits images by filling point sources with random values from the surrounding pixel region. It also provides an interactive tool to combine FITS images filtered on different scales.

satcand applies theoretical constraints of orbital stability and tidal migration to KOI exomoon candidates. The package can evaluate the tidal migration within a Sun-Earth-Moon system, plot angular velocity over time, and calculate the migration time scale (T1) and the total migration time scale, among other things. In addition to the theoretical constraints, observational constraints can be applied.

Characterize and understandOpen Clusters(OCs) allow us to understand better properties and mechanisms about the Universe such as stellar formation and the regions where these events occur. They also provide information about stellar processes and the evolution of the galactic disk.

In this paper, we present a novel method to characterize OCs. Our method employs a model built on Artificial Neural Networks(ANNs). More specifically, we adapted a state of the art model, the Deep Embedded Clustering(DEC) model for our purpose. The developed method aims to improve classical state of the arts techniques. We improved not only in terms of computational efficiency (with lower computational requirements), but inusability (reducing the number of hyperparameters to get a good characterization of the analyzed clusters). For our experiments, we used the Gaia DR2 database as the data source, and compared our model with the clustering technique K-Means. Our method achieves good results, becoming even better (in some of the cases) than current techniques.

redshifts collects all unique spectroscopic redshifts from online databases such as VizieR and NED. It can perform a flexible search within a radius of a given set of (RA, DEC) coordinates and uses column names and descriptions (including UCD keywords) to identify columns containing spectroscopic redshifts or velocities. It weeds out photometric redshifts and duplicates and returns a unique list of best spectroscopic redshift measurements. redshifts can be used standalone from the terminal, and can take a number of optional command line arguments, or from Python.

spalipy performs detection-based astronomical image registration in Python. A source image is transformed to the pixel-coordinate system of a template image using their respective detections as tie-points by finding matching quads of detections. spalipy also includes an optional additional warping of the initial affine transformation via splines to achieve accurate registration in the case of non-homogeneous coordinate transforms. This is particularly useful in the case of optically distorted or wide field-of-view images.

hfs_fit performs parameter optimization in the analysis of emission line hyperfine structure (HFS). The code uses a simulated annealing algorithm to optimize the magnetic dipole interaction constants, electric quadrupole interaction constants, Voigt profile widths and the center of gravity wavenumber for a given emission line profile. The fit can be changed visually with sliders for parameters, which is useful when HFS constants are unknown.

21cmDeepLearning extracts the underlying matter density map from a 21 cm intensity field by making use of a convolutional neural network (CNN) with the U-Net architecture; the software is implemented in Pytorch. The astrophysical parameters of the simulations can be predicted with a secondary CNN. The simulations of matter density and 21 cm maps are performed with the code 21cmFAST (ascl:1102.023).

The synchrofit (synchrotron fitter) package implements a reduced dimensionality parameterisation of standard synchrotron spectrum models, and provides fitting routines applicable for active galactic nuclei and supernova remnants. The Python code includes the Jaffe-Parola model (JP), Kardashev-Pacholczyk model (KP), and continuous injection models (CI/KGJP) for both constant or Maxwell-Boltzmann magnetic field distributions. An adaptive maximum likelihood algorithm is invoked to fit these models to multi-frequency radio observations; the adaptive mesh is customisable for either optimal precision or computational efficiency. Functions are additionally provided to plot the fitted spectral model with its confidence interval, and to derive the spectral age of the synchrotron emitting particles.

USNO/AE98 contains ephemerides for fifteen of the largest asteroids that The Astronomical Almanac has used since its 2000 edition. These ephemerides are based on the Jet Propulsion Laboratory (JPL) planetary ephemeris DE405 and, thus, aligned to the International Celestial Reference System (ICRS). The data cover the period from 1799 November 16 (JD 2378450.5) through 2100 February 1 (JD 2488100.5). The internal uncertainty in the mean longitude at epoch, 1997 December 18, ranges from 0.05 arcseconds for 7 Iris through 0.22 arcseconds for 65 Cybele, and the uncertainty in the mean motion varies from 0.02 arcseconds per century for 4 Vesta to 0.14 arcseconds per century for 511 Davida.

The Astronomical Almanac has published ephemerides for 1 Ceres, 2 Pallas, 3 Juno, and 4 Vesta since its 1953 edition. Historically, these four asteroids have been observed more than any of the others. Ceres, Pallas, and Vesta deserve such attention because as they are the three most massive asteroids, the source of significant perturbations of the planets, the largest in linear size, and among the brightest main belt asteroids. Studying asteroids may provide clues to the origin and primordial composition of the solar system, data for modeling the chaotic dynamics of small solar system bodies, and assessments of potential collisions. Therefore, USNO/AE98 includes more than the traditional four asteroids.

The following criteria were used to select main belt asteroids for USNO/AE98:

Diameter greater than 300 km, presumably among the most massive asteroids
Excellent observing history and discovered before 1850
Largest in their taxonomic class
The massive asteroids included may be studied for their perturbing effects on the planets while those with detailed observing histories may be used to evaluate the accuracy limits of asteroid ephemerides. The fifteen asteroids that met at least one of these criteria are

1 Ceres (new mass determination)
2 Pallas (new mass determination)
3 Juno
4 Vesta (new mass determination)
6 Hebe
7 Iris
8 Flora
9 Metis
10 Hygiea
15 Eunomia
16 Psyche
52 Europa
65 Cybele
511 Davida
704 Interamnia
The refereed paper by Hilton (1999, Astron. J. 117, 1077) describes the USNO/AE98 asteroid ephemerides in detail. The associated USNO/AA Tech Note 1998-12 includes residual plots for all fifteen asteroids and a comparison between these ephemerides and those used in The Astronomical Almanac through 1999.

Software to compact, read, and interpolate the USNO/AE98 asteroid ephemerides is also available. It is written in C and designed to work with the C edition of the Naval Observatory Vector Astrometry Software (NOVAS). The programs could be used with tabular ephemerides of other asteroids as well. The associated README file provides the details of this system.

FLARE, a parallel code written in Python, generates 100,000 Fast Radio Bursts (FRB) using the Monte Carlo method. The FRB population is diverse and includes sporadic FRBs, repeaters, and periodic repeaters. However, less than 200 FRBs have been detected to date, which makes understanding the FRB population difficult. To tackle this problem, FLARE uses a Monte Carlo method to generate 100,000 realistic FRBs, which can be analyzed later on for further research. It has the capability to simulate FRB distances (based on the observed FRB distance range), energies (based on the "flaring magnetar model" of FRBs), fluences, multi-wavelength counterparts (based on x-ray to radio fluence ratio of FRB 200428), and other properties. It analyzes the resulting synthetic FRB catalog and displays the distribution of their properties. It is fast (as a result of parallel code) and requires minimal human interaction. FLARE is, therefore, able to give a broad picture of the FRB population.

GLEAM (Galaxy Line Emission and Absorption Modeling) fits Gaussian models to emission and absorption lines in large samples of 1D galaxy spectra. The code is tailored to work well without much human interaction on optical and infrared spectra in a wide range of instrument setups and signal-to-noise regimes. gleam will create a fits table with Gaussian line measurements, including central wavelength, width, height and amplitude, as well as estimates for the continuum under the line and the line flux, luminosity, equivalent width and velocity width. gleam will also, optionally, make plots of the spectrum with fitted lines overlaid.

BALRoGO (Bayesian Astrometric Likelihood Recovery of Galactic Objects) handles data from the Gaia space mission. It extracts galactic objects such as globular clusters and dwarf galaxies from data contaminated by interlopers using a combination of Bayesian and non-Bayesian approaches. It fits proper motion space, surface density, and the object center. It also provides confidence regions for the color-magnitude diagram and parallaxes.

PyAutoFit supports advanced statistical methods such as massively parallel non-linear search grid-searches, chaining together model-fits and sensitivity mapping. It is a Python-based probabilistic programming language which composes and fits models using a range of Bayesian inference libraries, such as emcee (ascl:1303.002) and dynesty (ascl:1809.013). It performs model composition and customization, outputting results, model-specific visualization and posterior analysis. Built for big-data analysis, results are output as a database which can be loaded after model-fitting is complete.

PyFstat performs F-statistic-based continuous gravitational wave (CW) searches and other CW data analysis tasks. It is built on top of the LALSuite library (ascl:2012.021), making that library's functionality more accessible through a Python interface; it also provides MCMC-based followup of promising candidates from wide-parameter-space searches.

extinction is an implementation of fast interstellar dust extinction laws in Python. It contains Cython-optimized implementations of empirical dust extinction laws found in the literature. Flux values can be reddened or dereddened using included functions, and all extinction laws accept a unit keyword to change the interpretation of the wavelength array from Angstroms to inverse microns. Part of this code originated in the specutils package (ascl:1902.012).

binaryoffset identifies the binary offset effect in images from any detector. The easiest input to work with is a dark or bias image that is spatially flat. The code can also be run on images that are not spatially flat, assuming that there is some model of the signal on the CCD that can be used to produce a residual image.

Piff models the point-spread function (PSF) across multiple detectors in the full field of view (FOV). Models can be built in chip coordinates or in sky coordinates if needed to account for the effects of astrometric distortion. The software can fit in either real or Fourier space, and can identify and excise outlier stars that are poor exemplars of the PSF according to some metric.

Multi_CLASS modifies the Boltzmann code CLASS (ascl:1106.020) to compute of the cross-tracer angular power spectra of the number count fluctuations for two different tracers of the underlying dark matter density field. In other words, it generalizes the standard nCl output option of CLASS to the case of two different tracers, for example, two different galaxy populations with their own redshift distribution, and galaxy and magnification bias parameters, among others.

Multi_CLASS also includes an implementation of the effect of primordial non-Gaussianities of the local type, parametrized by the parameter f_NL (following the large-scale structure convention), on the effective bias of the tracers. There is also the possibility of having a tilted non-Gaussian correction, parametrized by n_NG, with a pivot scale determined by k_pivot_NG. The package includes galaxy redshift distributions for forthcoming galaxy surveys, with the ease of choosing between them (or an input file) from the parameters input file (e.g., multi_explanatory.ini). In addition, Multi_CLASS includes the possibility of using resolved gravitational wave events as a tracer.

RASSINE normalizes merged 1D spectra using the concept of convex hulls. The code uses six parameters that can be fine-tuned, and provides an interactive interface, including graphical feedback, for easily choosing the parameters. RASSINE can also provide a first guess for the parameters that are derived directly from the merged 1D spectrum based on previously performed calibrations.

lensingGW simulates lensed gravitational waves in ground-based interferometers from arbitrary compact binaries and lens models. Its algorithm resolves strongly lensed images and microimages simultaneously, such as the images resulting from hundreds of microlenses embedded in galaxies and galaxy clusters. It is based on Lenstronomy (ascl:1804.012).

MOSAIC (Multipole Operators in Symbols, Automatically Improved and Condensed) automatically produces, verifies, and optimizes computer code for Fast Multipole Method (FMM) operators. It is based on a symbolic algebra library, and can produce code for any expansion order and be extended to use any basis or kernel function. The code applies algebraic modifications to reduce the number of floating-point operations and can symbolically verify correctness.

HUAYNO implements integrators derived from second order Hamiltonian splitting for N-body dynamics. This integration scheme conserves energy and momentum with little or no systematic drift. The code uses an explicit but approximate formula for the time symmetrization that is compatible with the use of individual time steps, making an iterative scheme unnecessary. HUAYNO is available as part of the AMUSE package (ascl:1107.007).

DaMaSCUS-SUN is a Monte Carlo tool simulating the process of solar reflection of dark matter (DM) particles. It provides precise estimates of the DM particle flux reflected by the Sun and passing through a direct detection experiment on Earth. One application is to compute exclusion limits for low DM masses based on nuclear and electron recoil experiments.

mirkwood uses supervised machine learning to model non-linearly mapping galaxy fluxes to their properties. Multiple models are stacked to mitigate poor performance by any individual model in a given region of the parameter space. The code accounts for uncertainties arising both from intrinsic noise in observations and from finite training data and incorrect modeling assumptions, and provides highly accurate physical properties from observations of galaxies as compared to traditional SED fitting.

OPUS (Observatoire de Paris UWS System) provides interoperable access to analysis and simulation codes on local machines or work clusters. This job control system was developed using the micro-framework bottle.py, and executes jobs asynchronously to better manage jobs with a long execution duration. The software follows the proposed IVOA Provenance Data Model to capture and expose the provenance information of jobs and results.

ForwardDiff implements methods to take derivatives, gradients, Jacobians, Hessians, and higher-order derivatives of native Julia functions (or any callable object, really) using forward mode automatic differentiation (AD).While performance can vary depending on the functions you evaluate, the algorithms implemented by ForwardDiff generally outperform non-AD algorithms in both speed and accuracy.

nway is a source cross-matching tool for arbitrarily many astronomical catalogs. It features Bayesian match probabilities based on astronomical sky coordinates (RA, DEC), works with arbitrarily many catalogs, and can handle varying errors. nway can also incorporate additional prior information, such as the magnitude or color distributions of the sources to match, and works accurately and fast in small areas and all-sky catalogs.

GalRotpy models the dynamical mass of disk-like galaxies and makes a parametric fit of the rotation curve by means of the composed gravitational potential of the galaxy. It can be used to check the presence of an assumed mass type component in a observed rotation curve, to determine quantitatively the main mass contribution in a galaxy by means of the mass ratios of a given set of five potentials, and to bound the contribution of each mass component given its gravitational potential parameters.

MUSE-PSFR reconstructs a PSF for the MUSE WFM-AO mode using telemetry data from SPARTA. The algorithm conducts a Fourier analysis of the laser-assisted ground layer adaptive optics (GLAO) residual phase statistics and has been test in end-to-end simulations. A sensitivity analysis was conducted to determine the required accuracy in terms of input parameters. MUSE-PSFR is capable of reconstructing the critical parameters of a PSF and can be used with MUSE 3D data by all MUSE users.

polgraw-allsky searches for almost monochromatic gravitational wave signals. This pipeline searches for continuous gravitational wave signals in time-domain data using the F-statistic on data from a network of detectors. The software generates a parameter space grid, conducts a coherent search for candidate signals in narrowband time segments, and searches for coincidences among different time segments. The pipeline also estimates the false alarm probability of coincidences and follows up on interesting outliers.

hardCORE calculates the minimum, maximum, and marginal core radius fractions (CRFmin, CRFmax, CRFmarg) for a solid exoplanet using only its mass and radius. Written in Python, the code is an efficient tool that is extremely fast to execute and perform inversions.

EqTide calculates the evolution of 2 bodies experiencing tidal evolution according to the "equilibrium tide" framework's "constant-phase-lag" and "constant-time-lag" models. The input file contains a list of options that can be set, as well as output parameters that print to a file during an integration. The example input files provide a guide for the syntax and grammar of EqTide.

CMasher provides a curated collection of scientific colormaps that are perceptually uniform sequential using the viscm package (ascl:2102.007). Most of them are color-vision deficiency friendly; they cover a wide range of different color combinations to accommodate for most applications. The package provides several alternatives to commonly used colormaps, such as chroma and rainforest for jet, sunburst for hot, neutral for binary, and fusion and redshift for coolwarm.

viscm is a Python tool for visualizing and designing colormaps using colorspacious and matplotlib.

Lightbeam simulates the 3D propagation of light through waveguides of arbitrary geometries. This code package is based off of the finite-differences beam propagation method, and employs a transverse adaptive mesh for extra computational efficiency. Also included are tools to simulate adaptive optics systems for use in conjunction with waveguides, useful in astronomical contexts for simulating coupling devices which transfer telescope light to the science instrument.

The Exoplanet Modeling and Analysis Center (EMAC) is a website which serves as a catalog, repository and integration platform for modeling and analysis resources focused on the study of exoplanet characteristics and environments. EMAC hosts user-submitted software ranging in category from planetary interior models to data visualization tools. Other features of EMAC include integrated web tools developed by the EMAC team in collaboration with the tools' original authors and video demonstrations of a growing number of hosted tools. EMAC aims to be a comprehensive repository for researchers to access a variety of exoplanet resources that can assist them in their work, and currently hosts a growing number of code bases, models, and tools. EMAC is a key project of the NASA GSFC Sellers Exoplanet Environments Collaboration (SEEC).

X-PSI simulates rotationally-modified (pulsed) surface X-ray emission from neutron stars, taking into account relativistic effects on the emitted radiation. This can then be used to perform Bayesian statistical inference on real or simulated astronomical data sets. Model parameters of interest may include neutron star mass and radius (useful to constrain the properties of ultradense nuclear matter) or the system geometry and properties of the hot emitting surface-regions. To achieve this, X-PSI couples code for likelihood functionality (simulation) with existing open-source software for posterior sampling (inference).

ThumbStack produces stacked maps and profiles, given catalogs of object positions and maps. It is designed for thermal and kinematic Sunyaev-Zel'dovich measurements. Based on Pixell (ascl:2102.003), it outputs 2D stacked maps and radial profiles for different filters (e.g., aperture photometry filters), as well as their covariances, estimated through several methods including bootstrap.

Pixell loads, manipulates, and analyzes maps stored in rectangular pixelization. It is mainly targeted for use with maps of the sky (e.g., CMB intensity and polarization maps, stacks of 21 cm intensity maps, binned galaxy positions or shear) in cylindrical projection, but its core functionality is more general. It extends numpy's ndarray to an ndmap class that associates a World Coordinate System (WCS) with a numpy array. It includes tools for Fourier transforms (through numpy or pyfft) and spherical harmonic transforms (through libsharp2 (ascl:1402.033)) of such maps and tools for visualization (through the Python Image Library).

MST (Minimum Spanning Tree) identifies velocity coherent large-scale filaments through ATLASGAL clumps. It can also isolate filaments embedded in a crowded position–position–velocity (PPV) space. One strength of this method is its repeatability compared to manual approaches.

spinOS calculates binary orbital elements. Given a set of radial velocity measurements of a spectroscopic binary and/or relative position measurement of an astrometric binary, spinOS fits an orbital model by minimizing a chi squared metric. These routines are neatly packaged in a graphical user interface, developed using tkinter, facilitating use. Minimization is achieved by default using a Levenberg-Marquardt algorithm from lmfit [ascl:1606.014]. A Markov Chain Monte Carlo option is available to sample the posterior probability distribution in order to estimate errors on the orbital elements.

Stratsi calculates stratified and vertically-shearing streaming instabilities. It solves one- and two-fluid linearized equations, and, for two-fluid models, also provides the parameters and analytic vertical structure and solves for equilibrium horizontal velocity profiles. It offers utilities and various plotting options, including plots to compare one- and two-fluid results, viscous results to inviscid results, and results from two different stokes numbers or two different metallicities. stratsi requires Dedalus (ascl:1603.015) and Eigentools (ascl:2101.017).

Eigentools is a set of tools for studying linear eigenvalue problems. The underlying eigenproblems are solved using Dedalus (ascl:1603.015), which provides a domain-specific language for partial differential equations. Eigentools extends Dedalus's EigenvalueProblem object and provides automatic rejection of unresolved eigenvalues, simple plotting of specified eigenmodes and of spectra, and computation of $\epsilon$-pseudospectra for any Differential-Algebraic Equations with user-specifiable norms. It includes tools to find critical parameters for linear stability analysis and is able to project eigenmode onto 2- or 3-D domain for visualization. It can also output projected eigenmodes as Dedalus-formatted HDF5 file to be used as initial conditions for Initial Value Problems, and provides simple plotting of drift ratios (both ordinal and nearest) to evaluate tolerance for eigenvalue rejection.

pyUPMASK is an unsupervised clustering method for stellar clusters that builds upon the original UPMASK (ascl:1504.001) package. Its general approach makes it applicable to analyses that deal with binary classes of any kind, as long as the fundamental hypotheses are met. The core of the algorithm follows the method developed in UPMASK but introducing several key enhancements that make it not only more general, they also improve its performance.

DarpanX computes reflectivity and other specular optical functions of a multilayer or single layer mirror for different energy and angles as well as to fit the XRR measurements of the mirrors. It can be used as a standalone package. It has also been implemented as a local module for XSPEC (ascl:9910.005), which is accessible through and requires PyXspec (ascl:2101.014), and can accurately fit experimentally measured X-ray reflectivity data. DarpanX is implemented as a Python 3 module and an API is provided to access the underlying algorithms.

PyXspec is an object oriented Python interface to the XSPEC (ascl:9910.005) spectral-fitting program. It provides an alternative to Tcl, the sole scripting language for standard Xspec usage. With PyXspec loaded, a user can run Xspec with Python language scripts or interactively at a Python shell prompt; everything in PyXspec is accessible by importing the package xspec into your Python script. PyXspec can be utilized in a Python script or from the command line of the plain interactive Python interpreter. PyXspec does not implement its own command handler, so it is not intended to be run as the Python equivalent of a traditional interactive XSPEC session (which is really an enhanced interactive Tcl interpreter).

Curvit produces light curves from UVIT (Ultraviolet Imaging Telescope) data. It uses the events list from the official UVIT L2 pipeline (version 6.3 onwards) as input. The makecurves function of curvit automatically detects sources from events list and creates light curves. Curvit provides source coordinates only in the instrument coordinate system. If you already have the source coordinates, the curve function of curvit can be used to create light curves. The package has several parameters that can be set by the user; some of these parameters have default values. Curvit is available on PyPI.

Octo-Tiger models mass transfer in binary systems using a Cartesian adaptive mesh refinement grid. It simulates the evolution of star systems based on a modified fast multipole method (FMM) on adaptive octrees. The code takes shock heating into account and uses the dual energy formalism with an ideal gas equation of state; it also conserves linear and angular momenta to machine precision. Octo-Tiger is implemented in C++ and is parallelized using the High Performance ParalleX (HPX) runtime system.

Nigraha identifies and evaluates planet candidates from TESS light curves. Using a combination of high signal to noise ratio (SNR) shallow transits, supervised machine learning, and detailed vetting, the neural network-based pipeline identifies planet candidates missed by prior searches. The pipeline runs in four stages. It first performs period finding using the Transit Least Squares (TLS) package and runs sector by sector to build a per-sector catalog. It then transforms the flux values in .fits lightcurve files to global/local views and write out the output in .tfRecords files, builds a model on training data, and saves a checkpoint. Finally, it loads a previously saved model to generate predictions for new sectors. Nigraha provides helper scripts to generate candidates in new sectors, thus allowing others to perform their own analyses.

The apogee package works with SDSS-III APOGEE and SDSS-IV APOGEE-2 data. It reads various data products and applies cuts, works with APOGEE bitmasks, and plots APOGEE spectra. It can generate model spectra for APOGEE spectra, and APOGEE model grids can be used to fit spectra. apogee includes some simple stacking functions and implements the effective selection function for APOGEE.

cFS is a platform and project independent reusable software framework and set of reusable applications developed by NASA Goddard Space Flight Center. There are three key aspects to the cFS architecture: a dynamic run-time environment, layered software, and a component based design, making it suitable for reuse on NASA flight projects and/or embedded software systems. This framework is used as the basis for the flight software for satellite data systems and instruments, but can also be used on other embedded systems. Modules of this package are used in NICER (Neutron star Interior Composition Explorer). The modules are available as separate downloads from SourceForge through the NASA cFS website.

EphemMatch reads in the period, epoch, positional, and other information of all the Kepler DR25 TCEs, as well as the cumulative KOI list, and lists of EBs from the Kepler Eclipsing Binary Working Group (http://keplerebs.villanova.edu) as well as several catalogs of EBs known from ground-based surveys. The code then performs matching to identify two different objects that have a statistically identical period and epoch (within some tolerance) and perform logic to identify which is the real source (the parent) and which is a false positive due to contamination from the parent (a child).

Mask galaxy is an automatic machine learning pipeline for detection, segmentation and morphological classification of galaxies. The model is based on the Mask R-CNN Deep Learning architecture. This model of instance segmentation also performs image segmentation at the pixel level, and has shown a Mean Average Precision (mAP) of 0.93 in morphological classification of spiral or elliptical galaxies.

ptemcee, pronounced "tem-cee", is a fork of Daniel Foreman-Mackey's emcee (ascl:1303.002) to implement parallel tempering more robustly. As far as possible, it is designed as a drop-in replacement for emcee. It is helpful for characterizing awkward, multi-modal probability distributions.

Avocado produces classifications of arbitrary astronomical transients and variable objects. It addresses the problem of biased spectroscopic samples by generating many lightcurves from each object in the original spectroscopic sample at a variety of redshifts and with many different observing conditions. The "augmented" samples of lightcurves that are generated are much more representative of the full datasets than the original spectroscopic samples.

radiowinds calculates the radio emission produced by the winds around stars. The code calculates thermal bremsstrahlung that is emitted from the wind, which depends directly on the density and temperature of the stellar wind plasma. The program takes input data in the form of an interpolated 3d grid of points (of the stellar wind) containing position, temperature and density data. From this it calculates the thermal free-free emission expected from the wind at a range of user-defined frequencies.

whereistheplanet predicts the locations of directly imaged companions (mainly exoplanets and brown dwarfs) based on past orbital fits to the data. This tool helps coordinate follow-up observations to characterize their properties, as precise pointing of the instrument is often needed. It uses orbitize! (ascl:1910.009) as a backend. whereistheplanet is available as a Python API, a command line tool, and a web form at whereistheplanet.com.

BAYES-LOSVD performs non-parametric extraction of the Line-Of-Sight Velocity Distributions in galaxies. Written in Python, it uses Stan (ascl:1801.003) to perform all the computations and provides reliable uncertainties for all the parameters of the model chosen for the fit. The code comes with a large number of features, including read-in routines for some of the most popular IFU spectrographs and surveys, such as ATLAS3D, CALIFA, MaNGA, MUSE-WFM, SAMI, and SAURON.

In cosmological N-body simulations, higher-order Lagrangian perturbation on the initial condition affects the formation of nonlinear structure. Using this code, the initial condition generated by Zel'dovich approximation (Lagrangian linear perturbation) for Gadget-2 code to initial condition with second- or third-order Lagrangian perturbation (2LPT, 3LPT).

EinsteinPy performs General Relativity and gravitational physics tasks, including geodesics plotting for Schwarzschild, Kerr and Kerr Newman space-time models, calculation of Schwarzschild radius, and calculation of event horizon and ergosphere for Kerr space-time. It can perform symbolic manipulations of various tensors such as Metric, Riemann, Ricci and Christoffel symbols. EinsteinPy also features hypersurface embedding of Schwarzschild space-time, and includes other utilities and functions. It is a community-developed package and is written in Python.

Magritte performs 3D radiative transfer modeling; though focused on astrophysics and cosmology, the techniques can also be applied more generally. The code uses a deterministic ray-tracer with a formal solver that currently focuses on line radiative transfer. Magritte can either be used as a C++ library or as a Python package.

DRAGraces (Data Reduction and Analysis for GRACES) reduces GRACES spectra taken with the Gemini North high-resolution spectrograph. It finds GRACES frames in a given directory, determines the list of bias, flat, arc and science frames, and performs the reduction and extraction. Written in IDL, DRAGraces is straightforward and easy to use.

HCGrid maps non-uniform radio astronomy data onto a uniformly distributed grid using a convolution-based algorithm on CPU-GPU heterogeneous platforms. The package has three modules; the initialization module initializes parameters needed for the calculation process, such as setting the size of the sampling space and output resolution. The gridding module uses a parallel ordering algorithm to pre-order the sampling points based on HEALPix on the CPU platform and uses an efficient two-level lookup table to speed up the acquisition of sampling points; it then accelerates convolution by using the high parallelism of GPU and through related performance optimization strategies based on CUDA architecture to further improve the gridding performance. The third module processes the results; it visualizes the gridding and exports the final products as FITS files.

SWIGLAL, a wrapper for and component of the LALSuite (ascl:2012.021) gravitational wave detection and analysis libraries, which are primarily written in C, makes LALSuite routines directly accessible to Python and Octave scripts.

LALSuite contains numerous gravitational wave analysis libraries. Written primarily in C, the libraries include math and signal analysis packages such as for vector manipulation, FFT, statistics, time-domain filtering, and numerical and signal injection routines. The libraries also include date and time and datatype factory routines, in addition to general and support tools and a variety of Python packages. Also included are packages for gravitational waveform and noise generation, burst gravitational wave data analysis, inspiral and ringdown CBC gravitational wave data analysis, pulsar and continuous wave gravitational wave data analysis, and Bayesian inference data analysis. Various wrappers and other tools are also included.

BlackHawk calculates the Hawking evaporation spectra of any black hole distribution. Written in C, the program enables users to compute the primary and secondary spectra of stable or long-lived particles generated by Hawking radiation of the distribution of black holes, and to study their evolution in time.

PyXel models astronomical X-ray imaging data; it provides a common set of image analysis tools for astronomers working with extended X-ray sources. PyXel can model surface brightness profiles from X-ray satellites using a variety of models and statistics. PyXel can, for example, fit a broken power-law model to a surface brightness profile, and fit a constant to the sky background level in the direction of the merging galaxy cluster.

SimCADO simulates observations with any NIR/Vis imaging system. Though the package was originally designed to simulate images for the European Extremely Large Telescope (ELT) and MICADO, with the proper input, it is capable of simulating observations from many different telescope and instrument configurations.

SLIT (Sparse Lens Inversion Technique) provides a method for inversion of lensed images in the frame of strong gravitational lensing. The code requires the input image along with lens mass profile and a PSF. The user then has to chose a maximum number of iterations after which the algorithm will stop if not converged and a image size ratio to the input image to set the resolution of the reconstructed source. Results are displayed in pyplot windows.

Pomegranate builds probabilistic models in Python that is implemented in Cython for speed. The code merges the easy-to-use API of scikit-learn with the modularity of probabilistic modeling, including general mixture and hidden Markov models and Bayesian networks, to allow users to specify complicated models without the need to be concerned about implementation details. The models are built from the ground up and natively support features such as multi-threaded parallelism and out-of-core processing.

Seaborn provides a high-level interface for drawing attractive statistical graphics. Written in Python, it builds on matplotlib and integrates closely with pandas data structures. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API allows the user to focus on what the different elements of the plots mean, rather than on the details of how to draw them.

Dolphin uniformly models large lens samples. It is a wrapper for Lenstronomy (ascl:1804.012), and features semi-automated modeling of a large sample of quasar and galaxy-galaxy lenses. Dolphin, written in Python, provides easy portability between local and MPI environments.

sedop is a Monte-Carlo minimization code designed to optimally construct spectral energy distributions (SEDs) for sources of ultraviolet and X-ray radiation employed in numerical simulations of reionization and radiative feedback.

TRAN_K2 searches for periodic transits in the photometric time series of the Kepler K2 mission. The search is made by considering stellar variability and instrumental systematics. TRAN_K2 is written in Fortran 77 and has a single input parameter file that can be edited by the user depending on the type of run and parameter ranges to be used.

Skye detects a statistically significant excess clustering of transit times, indicating that there are likely systematics at specific times that cause many false positive detections, for the Kepler DR25 planet candidate catalog. The technique could be used for any survey looking to statistically cull false alarms.

MADLens produces non-Gaussian cosmic shear maps at arbitrary source redshifts. A MADLens simulation with only 256^3 particles produces convergence maps whose power agree with theoretical lensing power spectra up to scales of L=10000. The code is based on a highly parallelizable particle-mesh algorithm and employs a sub-evolution scheme in the lensing projection and a machine-learning inspired sharpening step to achieve these high accuracies.

HydroCode1D is a 1D finite volume code that can run any problem with 1D or 2D/3D spherical symmetry including external gravity or self-gravity. The program provides, depending on the configuration, output files that contain the midpoint position, density, velocity and pressure for each cell in the grid (in SI units). The program will by default use all available threads (as given by the environment variable OMP_NUM_THREADS). This can be overwritten by giving the desired number of threads as a command line argument to the program.

LIFELINE (LIne proFiles in massivE coLliding wInd biNariEs) simulates the X-ray lines profile in colliding wind binaries. The code is self-consistent and computes the distribution of the wind velocity, the characterization of the wind shock region, and the line profile. In addition to perform the overall computation, LIFELINE can use a pre-computed velocity distribution to compute the shock characteristics and the line profile, or use pre-computed shock characteristics and velocity distributions to compute only the line profile.

EOS is an analytical equation of state which models high pressure theory and fits well to the experimental data of ∊-Fe, SiO2, Mg2SiO4, and the Earth. The cold part of the EOS is modeled after the Varpoly EOS. The thermal part is based on a new formalism of the Gruneisen parameter, which improves behavior from earlier models and bridges the gap between elasticity and thermoelasticity. The EOS includes an expanded state model, which allows for the accurate modeling of material vapor curves.

The DR25 Kepler Robovetter is a robotic decision-making code that dispositions each Threshold Crossing Event (TCE) from the final processing (DR 25) of the Kepler data into Planet Candidates (PCs) and False Positives (FPs). The Robovetter provides four major flags to designate each FP TCE as Not Transit-Like (NTL), a Stellar Eclipse (SS), a Centroid Offset (CO), and/or an Ephemeris Match (EM). It produces a score ranging from 0.0 to 1.0 that indicates the Robovetter's disposition confidence, where 1.0 indicates strong confidence in PC, and 0.0 indicates strong confidence in FP. Finally, the Robovetter provides comments in a text string that indicate the specific tests each FP TCE fails and provides supplemental information on all TCEs as necessary.

MLC_EPGs classifies intermediate redshift (z = 0.3–0.8) emission line galaxies as star-forming galaxies, composite galaxies, active galactic nuclei (AGN), or low-ionization nuclear emission regions (LINERs). It uses four supervised machine learning classification algorithms: k-nearest neighbors (KNN), support vector classifier (SVC), random forest (RF), and a multi-layer perceptron (MLP) neural network. For input features, it uses properties that can be measured from optical galaxy spectra out to z < 0.8—[O III]/Hβ, [O II]/Hβ, [O III] line width, and stellar velocity dispersion—and four colors (u−g, g−r, r−i, and i−z) corrected to z = 0.1.

Given a series of radial velocities as a function of time for a star in a binary system, BinaryStarSolver solves for various orbital parameters. Namely, it solves for eccentricity (e), argument of periastron (ω), velocity amplitude (K), long term average radial velocity (γ), and orbital period (P). If the orbital parameters of a primary star are already known, it can also find the orbital parameters of a companion star, with only a few radial velocity data points.

Sengi enables online viewing of the spectral outputs of stellar population synthesis (SPS) codes. Typical SPS codes require significant disk space or computing resources to produce spectra for simple stellar populations with arbitrary parameters, making it difficult to present their results in an interactive, web-friendly format. Sengi uses Non-negative Matrix Factorisation (NMF) and bilinear interpolation to estimate output spectra for arbitrary values of stellar age and metallicity; this reduces the disk requirements and computational expense, allowing Sengi to serve the results in a client-based Javascript application.

The NOIRLab Source Catalog Generator generates the NOIRLab Source Catalog (NSC), a catalog of all publicly available imagining data in the NOIRLab Astro Data Archive. The second data release (DR2) of the archive contains over 3.9 billion unique objects, 68 billion individual source measurements, covers 35,000 square degrees of the sky, has depths of 23rd magnitude in most broadband filters with 1-2% photometric precision, and astrometric accuracy of 7 mas. NSCG is written in Python and IDL. Three main steps generate the NSC: (1) Source Extractor (ascl:1010.064) is used to detect and measure sources in individual images; (2) astrometrics are calibrated with Gaia DR2 and photometric calibration using large public photometric catalogs such as Pan-STARRS1 and ATLAS-Refcat2; and, (3) measurements are clustered into unique objects, averaging photometric and morphological properties, and calculating proper motions and photometric variability indices.

getsf extracts sources and filaments in astronomical images by separating their structural components, and is designed to handle multi-wavelength sets of images and very complex filamentary backgrounds. The method spatially decomposes the original images and separates the structural components of sources and filaments from each other and from their backgrounds, flattening their resulting images. It spatially decomposes the flattened components, combines them over wavelengths, and detects the positions of sources and skeletons of filaments. Finally, getsf measures the detected sources and filaments and creates the output catalogs and images. This universal and fully automated method has a single user-definable free parameter, which reduces to a minimum dependence of its results on the human factor.

ExoPix is a collection of tutorials aimed at illustrating the imaging of exoplanets with the James Webb Space Telescope (JWST). ExoPix tutorials are meant to demonstrate the application of the PSF-subtraction algorithm pyKLIP (ascl:1506.001) to simulated JWST NIRCAM data. We provide simple walkthroughs of pyKLIP’s ability to reveal exoplanets, compute contrast curves, and measure exoplanet astrometry and photometry in imaged extrasolar systems.

DDCalc performs various dark matter direct detection calculations, including signal rate predictions, constraints on light DM, and likelihoods for several experiments. It offers eighteen non-relativistic effective operators to describe velocity and momentum transfer, and elastic scattering of DM particles off nucleons, and has an extended detector interface.

DarkBit computes dark matter constraints on extensions to the Standard Model of particle physics. Written in the GAMBIT (ascl:1708.030) framework, it seamlessly integrates with other tools in the statistical fitting framework; it is also available as a standalone tool. It offers a signal yield calculator for gamma-ray observations, provides likelihoods for arbitrary combinations of spin-independent and spin-dependent scattering processes, and provides a general solution for studying complex particle physics models that predict dark matter annihilation to a multitude of final states.

CWITools analyzes integral field spectroscopy data from the Palomar and Keck Cosmic Web Imagers, and can be adapted for any three-dimensional integral field spectroscopy data. The package is modular, allowing users to construct data analysis pipelines to suit their own scientific needs, and includes tools for reducing data cubes, extracting a target signal, making emission maps, spectra, and other products. It also fits emission line and radial profiles and obtains final scalar quantities such as size and luminosity, among other tasks. It also contains helper functions that can, for example, obtain the wavelength axis from a 3D header, and create an auto-populated list of nebular emission lines or sky lines.

Kiauhoku interacts with, manipulates, and interpolates between stellar evolutionary tracks in a model grid. It was built for interacting with YREC models, but other stellar evolution model grids, including MIST, Dartmouth, and GARSTEC, are also available.

DeepShadows uses a convolutional neural networks (CNNs) to separate low-surface-brightness galaxies (LSBGs) from artifacts (such as Galactic cirrus and star-forming regions) in survey images. The model is trained and tested on labeled LSBGs and artifacts from the Dark Energy Survey and demonstrates that CNNs offer a promising path in the quest to study the low-surface-brightness universe.

PNICER estimates extinction for individual sources and creates extinction maps using unsupervised machine learning algorithms. Extinction towards single sources is determined by fitting Gaussian Mixture Models along the extinction vector to (extinction-free) control field observations. PNICER also offers access to the well-established NICER technique in a simple unified interface and is capable of building extinction maps including the NICEST correction for cloud substructure.

The ACStools package contains Python tools to work with data from the Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS). The package has several calibration utilities and a zeropoints calculator, can detect satellite trails, and offers destriping, polarization, and photometric tools.

reproject implements image reprojection (resampling) methods for astronomical images using various techniques via a uniform interface. Reprojection re-grids images from one world coordinate system to another (for example changing the pixel resolution, orientation, coordinate system). reproject works on celestial images by interpolation, as well as by finding the exact overlap between pixels on the celestial sphere. It can also reproject to/from HEALPIX projections by relying on the astropy-healpix package.

GPCAL performs instrumental polarization calibration in very long baseline interferometry (VLBI) data. It enhances the calibration accuracy by enabling users to fit the model to multiple calibrators data simultaneously and to take into account the calibrators linear polarization structures instead of using the conventional similarity assumption. GPCAL is based on AIPS (ascl:9911.003) and uses ParselTongue (ascl:1208.020) to run AIPS tasks.

HSTCosmicrays finds and characterizes cosmic rays found in dark frames (exposures taken with the shutter closed) taken with instruments on the Hubble Space Telescope (HST). Dark exposures are obtained routinely by all the Hubble Space Telescope instruments for calibration. The main processing pipeline runs locally or in the cloud on AWS utilizing the HST Public Dataset.

REBOUNDx incorporates additional physics into REBOUND (ascl:1110.016) simulations. Users can add effects from a list of pre-implemented astrophysical forces or contribute new ones. The main code is written in C, and a Python wrapper is provided for interfacing with other libraries. The REBOUNDx source code is machine independent and a binary format to save REBOUNDx configurations interfaces with the SimulationArchive class in REBOUND, making it possible to share and reproduce results bit by bit.

SCINTOOLS (SCINtillation TOOLS) simulates and analyzes pulsar scintillation data. The code can be used for processing observed dynamic spectra, computing secondary spectra and ACFs, measuring scintillation arcs, simulating dynamic spectra, and modeling pulsar transverse velocities through scintillation arcs or diffractive timescales.

Clustering is a modified version of the single-pulse sifting algorithm RRATrap (ascl:2011.017) combined with DBSCAN codes to cluster single pulse events.

RRATtrap is a single-pulse sifting algorithm to identify Rotating Radio Transients (RRATs) and transients using output from the PRESTO (ascl:1107.017) routine single_pulse_search.py. It can be integrated into pulsar survey data analysis pipelines and, in addition to finding RRATs, it can also identify Fast Radio Bursts.

GoFish exploits the known rotation of a protoplanetary disk to shift all emission to a common line center in order to stack them, increasing the signal-to-noise of the spectrum, detecting weaker lines, or super-sampling the spectrum to better resolve the line profile.

EvapMass predicts the minimum masses of planets in multi-planet systems using the photoevaporation-driven evolution model. The planetary system requires both a planet above and below the radius gap to be useful for this test. EvapMass includes an example Jupyter notebook for the Kepler-36 system. EvalMass can be used to identify TESS systems that warrant radial-velocity follow-up to further test the photoevaporation model.

SEDkit constructs and analyzes simple spectral energy distributions (SED). This collection of pure Python modules creates individual SEDs or SED catalogs from spectra and/or photometry and calculates fundamental parameters (fbol, Mbol, Lbol, Teff, mass, log(g)).

Tidally Locked Coordinates converts global climate model (GCM) output from standard/Earth-like coordinates into a tidally locked coordinate system. The transformations in Tidally Locked Coordinates are useful for plotting and analyzing GCM simulations of slowly rotating tidally locked planets such as Earth-like planets inside the habitable zone of small stars. They can be used to leverage the fact that a slowly rotating planet's climate will start to look approximately symmetric about the axis of insolation.