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.