stella creates and trains a neural network to identify stellar flares. Within stella, users can simulate flares as a training set, run a neural network, and feed in their own data to the neural network model. The software returns a probability at each data point as to whether that data point is part of a flare; the code can also characterize the flares identified.

stellaR accesses and manipulates publicly available stellar evolutionary tracks and isochrones from the Pisa low-mass database. It retrieves and plots the required calculations from CDS, constructs by interpolation tracks or isochrones of compositions different to the ones available in the database, constructs isochrones for age not included in the database, and extracts relevant evolutionary points from tracks or isochrones.

Cosmic ray electrons scatter on the photon fields around stars, including the sun, to create gamma rays by the inverse Compton effect. Stellarics computes the spectrum and angular distribution of this emission. The software also includes general-purpose routines for inverse Compton scattering on a given electron spectrum, for example for interstellar or astrophysical source modelling.

stellarWakes uses stellar kinematic data to search for dark matter (DM) subhalos through their gravitational perturbations to the stellar phase-space distribution.

StelNet predicts mass and age from absolute luminosity and effective temperature for stars with close to solar metallicity. It uses a Deep Neural Network trained on stellar evolutionary tracks. The underlying model makes no assumption on the evolutionary stage and includes the pre-main sequence phase. A mix of models are trained and bootstrapped to quantify the uncertainty of the model, and data is through all trained models to provide a predictive distribution from which an expectation value and uncertainty level can be estimated.

StePar computes the stellar atmospheric parameters T_eff, log g, [Fe/H], and ξ of FGK-type stars using the Equivalent Width (EW) method. The code implements a grid of MARCS model atmospheres and uses the MOOG radiative transfer code (ascl:1202.009) and TAME (ascl:1503.003). StePar uses a Downhill Simplex minimization algorithm, running it twice for any given star, to compute the stellar atmospheric parameters.

SteParSyn infers stellar atmospheric parameters (Teff, log g, [Fe/H], and Vbroad) of FGKM-type stars using the spectral synthesis method. The code uses the MCMC sampler emcee (ascl:1303.002) in conjunction with an spectral emulator that can interpolate spectra down to a precision < 1%. A grid of synthetic spectra that allow the user to characterize the spectra of FGKM-type stars with parameters in the range of 3500 to 7000 K in Teff, 0.0 to 5.5 dex in log g, and −2.0 to 1.0 dex in [Fe/H] is also provided.

stepped_luneburg investigates the scattered light properties of a Luneburg lens approximated as a series of concentric shells with discrete refractive indices. The optical Luneburg lens has promising applications for low-cost, continuous all-sky monitoring to obtain transit light curves of bright, nearby stars. This code implements a stack-based algorithm that tracks all reflected and refracted rays generated at each optical interface of the lens as described by Snell's law. The Luneburg lens model parameters, such as number of lens layers, the power-law that describes the refractive indices, the number of incident rays, and the initial direction of the incident wavefront can be altered to optimize lens performance. The stepped_luneburg module can be imported within the Python environment or used with scripting, and it is accompanied by two other modules, enc_int and int_map, that help the user to determine the resolving power of the lens and the strength of scattered light haloes for the purpose of quality assessment.

StePS (Stereographically Projected Cosmological Simulations) compactifies the infinite spatial extent of the Universe into a finite sphere with isotropic boundary conditions to simulate the evolution of the large-scale structure. This eliminates the need for periodic boundary conditions, which are a numerical convenience unsupported by observation and which modifies the law of force on large scales in an unrealistic fashion. StePS uses stereographic projection for space compactification and naive O(N2) force calculation; this arrives at a correlation function of the same quality more quickly than standard (tree or P3M) algorithms with similar spatial and mass resolution. The N2 force calculation is easy to adapt to modern graphics cards, hence StePS can function as a high-speed prediction tool for modern large-scale surveys.

The sterile neutrino production code sterile-dm incorporates new elements to the calculations of the neutrino opacity at temperatures 10 MeV ≤ T ≤ 10 GeV and folds the asymmetry redistribution and opacity calculations into the sterile neutrino production computation, providing updated PSDs for the range of parameters relevant to the X-ray excess. The code requires several data files, which are included. With each run, sterile-dm creates a new output sub-directory that contains a parameter file listing the mass, mixing angle, initial lepton asymmetry and other information, a state file, which includes, among other states, the temperature and FRW coordinate time, and a set of snapshot files, one for each line in the state file.

STF is a general structure finder designed to find halos, subhaloes, and tidal debris in N-body simulations. The current version is designed to read in "particle data" (that is SPH N-body data), but a simple modification of the I/O can have it read grid data from Grid based codes.

This code has been updated and renamed to VELOCIraptor-STF (ascl:1911.020).

stginga customizes Ginga to aid data analysis for the data supported by STScI (e.g., HST or JWST). For instance, it provides plugins and configuration files that understand HST and JWST data products.

STiC is a MPI-parallel non-LTE inversion code for observed full-Stokes observations. The code processes lines from multiple atoms in non-LTE, including partial redistribution effects of scattered photons in angle and frequency of scattered photons (PRD), and can be used with model atmospheres that have a complex depth stratification without introducing artifacts.

STIFF converts scientific FITS images to the more popular TIFF format for illustration purposes. Most FITS readers and converters do not do a proper job at converting FITS image data to 8 bits. 8-bit images stored in JPEG, PNG or TIFF files have the intensities implicitly stored in a non-linear way. Most current FITS image viewers and converters provide the user an incorrect translation of the FITS image content by simply rescaling linearly input pixel values. A first consequence is that the people working on astronomical images usually have to apply narrow intensity cuts or square-root or logarithmic intensity transformations to actually see something on their deep-sky images. A less obvious consequence is that colors obtained by combining images processed this way are not consistent across such a large range of surface brightnesses. Though with other software the user is generally afforded a choice of nonlinear transformations to apply in order to make the faint stuff stand out more clearly in the images, with the limited selection of choices provides, colors will not be accurately rendered, and some manual tweaking will be necessary. The purpose of STIFF is to produce beautiful pictures in an automatic and consistent way.

The STIL Tool Set is a set of command-line tools based on STIL, the Starlink Tables Infrastructure Library. It deals with the processing of tabular data; the package has been designed for, but is not restricted to, astronomical tables such as object catalogues. Some of the tools are generic and can work with multiple formats (including FITS, VOTable, CSV, SQL and ASCII), and others are specific to the VOTable format. In some ways, STILTS forms the command-line counterpart of the GUI table analysis tool TOPCAT. The package is robust, fully documented, and designed for efficiency, especially with very large datasets.

Facilities offered include:

- format conversion
- crossmatching
- plotting
- column calculation and rearrangement
- row selections
- data and metadata manipulation and display
- sorting
- statistical calculations
- histogram calculation
- data validation
- VO service access

A powerful and extensible expression language is used for specifying data calculations. These facilities can be put together in very flexible and efficient ways. For tasks in which the data can be streamed, the size of table STILTS can process is effectively unlimited. For other tasks, million-row tables usually do not present a problem. STILTS is written in pure Java (J2SE1.5 or later), and can be run from the command line or from Jython, or embedded into java applications. It is released under the GPL.

stimela provides a system-agnostic scripting framework for simulating, processing, and imaging radio interferometric data. The framework executes radio interferometry related tasks such as imaging, calibration, and data synthesis in Docker containers using Python modules. stimela offers a simple interface to packages that perform these tasks rather than doing any data processing, synthesis or analysis itself. stimela only requires Docker and Python. Moreover, because of Docker, a stimela script runs the same way (in the same iso­lated environment) regardless of the host machine’s settings, thus providing a user-friendly and modular scripting environment that gives general users easy access to novel radio interferometry calibration, imaging, and synthesis packages.

Stingray is a spectral-timing software package for astrophysical X-ray (and more) data. The package merges existing efforts for a (spectral-)timing package in Python and is composed of a library of time series methods (including power spectra, cross spectra, covariance spectra, and lags); scripts to load FITS data files from different missions; a simulator of light curves and event lists that includes different kinds of variability and more complicated phenomena based on the impulse response of given physical events (e.g. reverberation); and a GUI to ease the learning curve for new users.

STOKES was designed to perform three-dimensional radiative transfer simulations for astronomical applications. The code also considers the polarization properties of the radiation. The program is based on the Monte-Carlo method and treats optical and ultraviolet polarization induced by scattering off free electrons or dust grains. Emission and scattering regions can be arranged in various geometries within the model space, the computed continuum and line spectra can be evaluated at different inclinations and azimuthal viewing angles.

STools contains a variety of simple tools for spectroscopy, such as reading an IRAF-formatted (multispec) echelle spectrum in FITS, measuring the wavelength of the center of a line, Gaussian convolution, deriving synthetic photometry from an input spectrum, and extracting and interpolating a MARCS model atmosphere (standard composition).

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).

Stream-stream analyzes the interaction between a stellar stream and a disrupting dark-matter halo. It requires galpy (ascl:1411.008), NEMO (ascl:1010.051), and the usual common scientific Python packages.

streamgap-pepper computes the effect of subhalo fly-bys on cold tidal streams based on the action-angle representation of streams. A line-of-parallel-angle approach is used to calculate the perturbed distribution function of a given stream segment by undoing the effect of all impacts. This approach allows one to compute the perturbed stream density and track in any coordinate system in minutes for realizations of the subhalo distribution down to 10^5 Msun, accounting for the stream's internal dispersion and overlapping impacts. This code uses galpy (ascl:1411.008) and the streampepperdf.py galpy extension, which implements the fast calculation of the perturbed stream structure.

StringFast implements a method for efficient computation of the C_l spectra induced by a network of strings, which is fast enough to be used in Markov Chain Monte Carlo analyses of future data. This code allows the user to calculate TT, EE, and BB power spectra (scalar [for TT and EE], vector, and tensor modes) for "wiggly" cosmic strings. StringFast uses the output of the public code CMBACT (ascl:1106.023). The properties of the strings are described by four parameters: Gμ—dimensionless string tension; v—rms transverse velocity (as fraction of c); α—"wiggliness"; ξ—comoving correlation length of the string network. It is written as a Fortran 90 module.

stringgen creates emulations of cosmic string maps with statistics similar to those of a single (or small ensemble) of reference simulations. It uses wavelet phase harmonics to calculate a compressed representation of these reference simulations, which may then be used to synthesize new realizations with accurate statistical properties, e.g., 2 and 3 point correlations, skewness, kurtosis, and Minkowski functionals.

StructureFunction determines the X-ray Structure Function of a population of Active Galactic Nuclei (AGN) for which two epoch X-ray observations are available and are separated by rest frame time interval. The calculation of the X-ray structure function is Bayesian. The sampling of the likelihood uses Stan (ascl:1801.003) for statistical modeling and high-performance statistical computation.

The Space Telescope Science Data Analysis System (STSDAS) is a software package for reducing and analyzing astronomical data. It is layered on top of IRAF and provides general-purpose tools for astronomical data analysis as well as routines specifically designed for HST data. In particular, STSDAS contains all the programs used for the calibration and reduction of HST data in the STScI post-observation processing pipelines.

An extension to synphot (ascl:1811.001), stsynphot implements synthetic photometry package for HST and JWST support. The software constructs spectra from various grids of model atmosphere spectra, parameterized spectrum models, and atlases of stellar spectrophotometry. It also simulates observations specific to HST and JWST, computes photometric calibration parameters for any supported instrument mode, and plots instrument-specific sensitivity curves and calibration target spectra.

Stuff is a program that simulates “perfect” astronomical catalogues. It generate object lists in ASCII which can read by the SkyMaker program to produce realistic astronomical fields. Stuff is part of the EFIGI development project.

SubGen generates Monte-Carlo samples of dark matter subhaloes. It fully describes the joint distribution of subhaloes in final mass, infall mass, and radius; it can be used to predict derived distributions involving combinations of these quantities, including the universal subhalo mass function, the subhalo spatial distribution, the gravitational lensing profile, the dark matter annihilation radiation profile and boost factor. SubGen works only for CDM subhaloes; for an extension of the code to also work with WDM subhaloes, see SubGen2 (ascl:2312.036).

The SubGen2 subhalo population generator works for both CDM and WDM of arbitrary DM particle mass. It can be used to generate a population of subhaloes according to the joint distribution of subhalo bound mass, infall mass and halo-centric distance in a halo of a given mass. SubGen2 is an extension to SubGen (ascl:2312.035), which works only for CDM subhaloes.

SubgridClumping derives the parameters for the global, in-homogeneous and stochastic clumping model and then computes the clumping factor for large low-resolution N-body simulations smoothed on a regular grid. Written for the CUBEP3M simulation, the package contains two main modules. The first derives the three clumping model parameters for a given small high-resolution simulation; the second computes a clumping factor cube (same mesh-size as input) for the three models for the given density field of a large low-resolution simulation.

sunbather simulates the upper atmospheres of exoplanets and their observational signatures. The code constructs 1D Parker wind profiles using p-winds (ascl:2111.011) to simulate these with Cloudy (ascl:9910.001), and postprocesses the output with a custom radiative transfer module to predict the transmission spectra of exoplanets.

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

SunnyNet learns the mapping the between LTE and NLTE populations of a model atom and predicts the NLTE populations based on LTE populations for an arbitrary 3D atmosphere. To use SunnyNet, one must already have a set of LTE and NLTE populations computed in 3D, to train the network. These must come from another code, as SunnyNet is unable to solve the formal problem. Once SunnyNet is trained, one can feed it LTE populations from a different 3D atmosphere, and obtain predicted NLTE populations. The NLTE populations can then be used to synthesize any spectral line that is included in the model atom. SunnyNet's output is a file with predicted NLTE populations. SunnyNet itself does not calculate synthetic spectra, but a sample script written in the Julia language that quickly computes Hα spectra is included.

sunpy is a community-developed free and open-source software package for solar physics and is an alternative to the SolarSoft (ascl:1208.013) data analysis environment. SunPy provides data structures for representing the most common solar data types (images, lightcurves, and spectra) and integration with the Virtual Solar Observatory (VSO) and the Heliophysics Event Knowledgebase (HEK) for data acquisition.

Sunrise is a Monte Carlo radiation transfer code for calculating absorption and scattering of light to study the effects of dust in hydrodynamic simulations of interacting galaxies. It uses an adaptive mesh refinement grid to describe arbitrary geometries of emitting and absorbing/scattering media, with spatial dynamical range exceeding 10⁴; it can efficiently generate images of the emerging radiation at arbitrary points in space and spectral energy distributions of simulated galaxies run with the Gadget (ascl:0003.001), Gasoline (ascl:1710.019), Arepo (ascl:1909.010), Enzo (ascl:1010.072) or ART codes. In addition to the monochromatic radiative transfer typically used by Monte Carlo codes, Sunrise can propagate a range of wavelengths simultaneously. This "polychromatic" algorithm gives significant improvements in efficiency and accuracy when spectral features are calculated.

These IDL codes create a thick magneto-static structure with parameters of a typical sunspot in a solar like photosphere - chromosphere. The variable parameters are field strength on the axis, radius, and Wilson depression (displacement of the atmosphere on the axis with respect to the field-free atmosphere). Output are magnetic field vector, pressure and density distributions with radius and height. The structure has azimuthal symmetry. The codes are relatively self explanatory and the download packages contain README files.

The superABC sampling method obtains cosmological constraints from supernova light curves using Approximate Bayesian Computation (ABC) without any likelihood assumptions. It provides an interface to two forward model simulations, SNCosmo (ascl:1611.017) and SNANA (ascl:1010.027), for supernova cosmology.

SuperBayeS is a package for fast and efficient sampling of supersymmetric theories. It uses Bayesian techniques to explore multidimensional SUSY parameter spaces and to compare SUSY predictions with observable quantities, including sparticle masses, collider observables, dark matter abundance, direct detection cross sections, indirect detection quantities etc. Scanning can be performed using Markov Chain Monte Carlo (MCMC) technology or even more efficiently by employing a new scanning technique called MultiNest (ascl:1109.006). which implements the nested sampling algorithm. Using MultiNest, a full 8-dimensional scan of the CMSSM takes about 12 hours on 10 2.4GHz CPUs. There is also an option for old-style fixed-grid scanning. A discussion forum for SuperBayeS is available.

The package combines SoftSusy, DarkSusy, FeynHiggs, Bdecay, MultiNest and MicrOMEGAs. Some of the routines and the plotting tools are based on CosmoMC (ascl:1106.025).

SuperBayeS comes with SuperEGO, a MATLAB graphical user interface tool for interactive plotting of the results. SuperEGO has been developed by Rachid Lemrani and is based on CosmoloGUI by Sarah Bridle.

SuperBoL calculates the bolometric lightcurves of Type II supernovae using observed photometry; it includes three different methods for calculating the bolometric luminosity: quasi-bolometric, direct, and bolometric correction. SuperBoL propagates uncertainties in the input data through the calculations made by the code, allowing for error bars to be included in plots of the lightcurve.

SUPERBOX is a particle-mesh code that uses moving sub-grids to track and resolve high-density peaks in the particle distribution and a nearest grid point force-calculation scheme based on the second derivatives of the potential. The code implements a fast low-storage FFT-algorithm and allows a highly resolved treatment of interactions in clusters of galaxies, such as high-velocity encounters between elliptical galaxies and the tidal disruption of dwarf galaxies, as sub-grids follow the trajectories of individual galaxies. SUPERBOX is efficient in that the computational overhead is kept as slim as possible and is also memory efficient since it uses only one set of grids to treat galaxies in succession.

SuperFreq numerically estimates the fundamental frequencies and orbital actions of pre-computed orbital time series. It is an implementation of a version of the Numerical Analysis of Fundamental Frequencies close to that by Monica Valluri, which itself is an implementation of an algorithm first used by Jacques Laskar.

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

SuperNNova performs photometric classification by leveraging recent advances in deep neural networks. It can train either a recurrent neural network or random forest to classify light-curves using only photometric information. It also allows additional information, such as host-galaxy redshift, to be incorporated to improve performance.

Flux-averaging justifies the use of the distance-redshift relation for a smooth universe in the analysis of type Ia supernova (SN Ia) data. Flux-averaging of SN Ia data is required to yield cosmological parameter constraints that are free of the bias induced by weak gravitational lensing. SN Ia data are converted into flux. For a given cosmological model, the distance dependence of the data is removed, then the data are binned in redshift, and placed at the average redshift in each redshift bin. The likelihood of the given cosmological model is then computed using "flux statistics''. These Fortran codes compute the likelihood of an arbitrary cosmological model [with given H(z)/H_0] using flux-averaged Type Ia supernova data.

Supernovae classifies supernovae using their light curves directly as inputs to a deep recurrent neural network, which learns information from the sequence of observations. Observational time and filter fluxes are used as inputs; since the inputs are agnostic, additional data such as host galaxy information can also be included.

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.

Superplot calculates and plots statistical quantities relevant to parameter inference from a "chain" of samples drawn from a parameter space produced by codes such as MultiNest (ascl:1109.006), BAYES-X (ascl:1505.027), and PolyChord (ascl:1502.011). It offers a graphical interface for browsing a chain of many variables quickly and can produce numerous kinds of publication quality plots, including one- and two-dimensional profile likelihood, three-dimensional scatter plots, and confidence intervals and credible regions. Superplot can also save plots in PDF format, create a summary text file, and export a plot as a pickled object for importing and manipulating in a Python interpreter.

SuperRad models ultralight boson clouds that arise through black hole superradiance. It uses numerical results in the relativistic regime combined with analytic estimates to describe the dynamics and gravitational wave signals of ultralight scalar or vector clouds. Written in Python, SuperRad includes a set of testing routines that check the internal consistency of the package; these tests mainly serve the purpose of ensuring functionality of the waveform model but can also be utilized to check that SuperRad works as intended.

SuperRAENN performs photometric classification of supernovae in the following categories: Type I superluminos supernovae, Type II, Type IIn, Type Ia and Type Ib/c. Though the code is optimized for use with complete (rather than realtime) light curves from the Pan-STARRS Medium Deep Survey, the classifier can be trained on other data. SuperRAENN can be used on a dataset containing both spectroscopically labelled and unlabelled SNe; all events will be used to train the RAENN, while labelled events will be used to train the random forest.