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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

petitRADTRANS (pRT) calculates transmission and emission spectra of exoplanets for clear and cloudy planets. It also incorporates an easy subpackage for running retrievals with nested sampling. It allows the calculation of emission or transmission spectra, at low or high resolution, clear or cloudy, and includes a retrieval module to fit a petitRADTRANS model to spectral data. pRT has two different opacity treatment modes. The low resolution mode runs calculations at λ/Δλ ≤ 1000 using the so-called correlated-k treatment for opacities. The high resolution mode runs calculations at λ/Δλ ≤ 10⁶, using a line-by-line opacity treatment.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

REFERENCES

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

simulateSearch simulates high time-resolution data in radio astronomy. The code is built around producing multiple binary data files that contain information on the radiometer noise and sources that are being simulated. These binary data files subsequently get combined and output PSRFITS
search mode files produced. The PSRFITS files can be processed using standard pulsar software packages such as PRESTO (ascl:1107.017).

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

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

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

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

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

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

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