Varstar Detect uses several numerical and statistical methods to filter and interpret the data obtained from TESS. It performs an amplitude test to determine whether a star is variable and if so, provides the characteristics of each star through phenomenological analysis of the lightcurve.

Menura simulates the interaction between a fully turbulent solar wind and various bodies of the solar system using a novel two-step approach. It is an advanced numerical tool for self-consistent modeling that bridges planetary science and plasma physics. Menura is built around a hybrid Particle-In-Cell solver, treating electrons as a charge-neutralising fluid, and ions as massive particles. It solves iteratively the particles’ dynamics, gathers particle moments at the nodes of a grid, at which the magnetic field is also computed, and then solves the Maxwell equations. This solver uses the popular Current Advance Method (CAM).

BHJet models steady-state SEDs of jets launched from accreting black holes. This semi-analytical, multi-zone jet model is applicable across the entire black hole mass scale, from black hole X-ray binaries (both low and high mass) to active galactic nuclei of any class (from low-luminosity AGN to flat spectrum radio quasars). It is designed to be more comparable than other codes to GRMHD simulations and/or RMHD semi-analytical solutions.

gphist performs Bayesian inference on the cosmological expansion history using Gaussian process priors. It is written in Python and includes driver programs to run inference calculations and plot the results. The code infers the cosmological expansion history using a Gaussian process prior, reads these ouputs, and performs checks to ensure they are indeed compatible. gphist then generates a single combined output file to plot expansion history inferences.

ShapeMeasurementFisherFormalism is used to study Fisher Formalism predictions on galaxy weak lensing for LSST Dark Energy Science Collaboration. It can create predictions with user-defined parameters for one or two galaxies simulated from GalSim (ascl:1402.009).

WeakLensingDeblending provides weak lensing fast simulations and analysis for the LSST Dark Energy Science Collaboration. It is used to study the effects of overlapping sources on shear estimation, photometric redshift algorithms, and deblending algorithms. Users can run their own simulations (of LSST and other surveys) or download the galaxy catalog and simulation outputs to use with their own code.

SNEWPY uses simulated supernovae data to generate a time series of neutrino spectral fluences at Earth or the total time-integrated spectral fluence. The code can also process generated data through SNOwGLoBES (ascl:2109.019) and collate its output into the observable channels of each detector. Data from core-collapse, thermonuclear, and pair-instability supernovae simulations are included in the package.

SNOwGLoBES (SuperNova Observatories with GLoBES) computes interaction rates and distributions of observed quantities for supernova burst neutrinos in common detector materials. The code provides a very simple and fast code and data package for tests of observability of physics signatures in current and future detectors, and for evaluation of relative sensitivities of different detector configurations. The event estimates are made using available cross-sections and parameterized detector responses. Water, argon, scintillator and lead-based configurations are included. The package makes use of GLoBES (ascl:2109.018). SNOwGLoBES is not intended to replace full detector simulations; however output should be useful for many types of studies, and simulation results can be incorporated.

GLoBES simulates long baseline neutrino oscillation experiments. The package features full incorporation of correlations and degeneracies in the oscillation parameter space, advanced routines for the treatment of arbitrary systematical errors, and user-defined priors, which allowsn for the inclusion of arbitrary external physical information. Its use of AEDL, the Abstract Experiment Definition Language, provides an easy way to define experimental setups. GLoBES also provides an interface for the simulation of non-standard physics, and offers predefined setups for many experiments, including Superbeams, Beta Beams, Neutrino factories, Reactors, and various detector technologies.

HTOF parses the intermediate data from Hipparcos and Gaia and fits astrometric solutions to those data. It computes likelihoods and parameter errors in line with the catalog and can reproduce five, seven, and nine (or higher) parameter fits to their astrometry.

SkyPy simulates the astrophysical sky. It provides functions that sample realizations of sources and their associated properties from probability distributions. Simulation pipelines are constructed from these models, while task scheduling and data dependencies are handled internally. The package's modular design, containing a library of physical and empirical models across a range of observables and a command line script to run end-to-end simulations, allows users to interface with external software.

unpopular is an implementation of the Causal Pixel Model (CPM) de-trending method to obtain TESS Full-Frame Image (FFI) light curves. The code, written in Python, models the systematics in the light curves of individual pixels as a linear combination of light curves from many other distant pixels and removes shared flux variations. unpopular is able to preserve sector-length astrophysical signals, allowing for the extraction of multi-sector light curves from the FFI data.

The Hough Stream Spotter (HSS) is a stream finding code which transforms individual positions of stars to search for linear structure in discrete data sets. The code requires only the two-dimensional plane of galactic longitude and latitude as input.

WimPyDD calculates accurate predictions for the expected rates in WIMP direct–detection experiments within the framework of Galilean–invariant non–relativistic effective theory. The object–oriented customizable Python code handles different scenarios including inelastic scattering, WIMP of arbitrary spin, and a generic velocity distribution of WIMP in the Galactic halo.

STAR-MELT extracts and identifies emission lines from FITS files by matching to a compiled reference database of lines. Line profiles are fitted and quantified, allowing for calculations of physical properties across each individual observation. Temporal variations in lines can readily be displayed and quantified. STAR-MELT is also useful for different applications of spectral analysis where emission line identification is required. Standard data formats for spectra are automatically compatible, with user-defined custom formats also available. Any reference database (atomic or molecular) can also be used for line identification.

Rubble implicitly models the local evolution of dust distributions in size, mass, and surface density by solving the Smoluchowski equation (also known as the coagulation-fragmentation equation) under given disk conditions. The Python package's robustness has been validated by a suite of numerical benchmarks against known analytical and empirical results. Rubble can model prescribed physical processes such as bouncing, modulated mass transfer, regulated dust loss/supply, probabilistic collisional outcomes based on velocity distributions, and more. The package also includes a toolkit for analyzing and visualizing results produced by Rubble.

Frankenstein (frank) fits the 1D radial brightness profile of an interferometric source given a set of visibilities. It uses a Gaussian process that performs the fit in <1 minute for a typical protoplanetary disc continuum dataset. Frankenstein can perform a fit in 2 ways, by running the code directly from the terminal or using the code as a Python module.

pyFFTW is a pythonic wrapper around FFTW (ascl:1201.015), the speedy FFT library. Both the complex DFT and the real DFT are supported, as well as on arbitrary axes of arbitrary shaped and strided arrays, which makes it almost feature equivalent to standard and real FFT functions of numpy.fft. Additionally, it supports the clongdouble dtype, which numpy.fft does not, and operating FFTW in multithreaded mode.

pyia provides tools for working with Gaia data. It accesses Gaia data columns as Quantity objects, i.e., with units (e.g., data.parallax will have units ‘milliarcsecond’)
, constructs covariance matrices for Gaia data, and generates random samples from the Gaia error distribution per source. pyia can also create SkyCoord objects from Gaia data and execute simple (small) remote queries via the Gaia science archive and automatically fetch the results.

SkyCalc-iPy (SkyCalc for interactive Python) accesses atmospheric emission and transmission data generated by ESO’s SkyCalc tool interactively with Python. This package is based on the command line tool by ESO for accessing spectra on the ESO SkyCalc server.

The e-MERLIN CASA Pipeline calibrates and processes data from the e-MERLIN radio interferometer. It works on top of CASA (ascl:1107.013) and can convert, concatenate, prepare, flag and calibrate raw to produce advanced calibrated products for both continuum and spectral line data. The main outputs of the data are calibration tables, calibrated data, assessment plots, preliminary images of target and calibrator sources and a summary weblog. The pipeline provides an easy, ready-to-use toolkit that delivers calibrated data in a consistent, clear, and repeatable way. A parameters file is used to control the pipeline execution, so optimization of the algorithms is straightforward and reproducible. Good quality images are usually obtained with minimum human intervention.

SoFiA 2 is a fully automated spectral-line source finding pipeline originally intended for the detection of galaxies in large HI data cubes. It is a reimplementation of parts of the original SoFiA pipeline (ascl:1412.001) in the C programming language and uses OpenMP for multithreading, making it substantially faster and more memory-efficient than its predecessor. At its core, SoFiA 2 uses the Smooth + Clip algorithm for source finding which operates by spatially and spectrally smoothing the data on multiple scales and applying a user-defined flux threshold relative to the noise level in each iteration. A wide range of useful preconditioning and post-processing filters is available, including noise normalization, flagging of artifacts and reliability filtering. In addition to global data products and source catalogs in different formats, SoFiA 2 can also generate cutout images and spectra for each individual detection.

DviSukta calculates the Spherically Averaged Bispectrum (SABS). The code is based on an optimized direct estimation method, is written in C, and is parallelized. DviSukta starts by reading the real space gridded data and performing a 3D Fourier transform of it. Alternatively, it starts by reading the data already in Fourier space. The grid spacing, number of k1 bins, number of n bins, and number of cos(theta) bins need to be specified in the input file.

The VOLK2 (VLBI Observation for transient Localization Keen Searcher) pipeline conducts single pulse searches and localization in regular VLBI observations as well as single pulse detections from known sources in dedicated observations. In VOLKS2, the search and localization are two independent steps. The search step takes the idea of geodetic VLBI post processing, which fully utilizes the cross spectrum fringe phase information to maximize the signal power. Compared with auto spectrum based method, it is able to extract single pulses from highly RFI contaminated data. The localization uses the geodetic VLBI solving methods, which derives the single pulse location by solving a set of linear equations given the relation between the residual delay and the offset to a priori position.

alpconv calculates the alp-photon conversion by calculating the degree of irregularity of the spectrum, in contract to some other methods that fit the source's spectrum with both null and ALP models and then compare the goodness of fit between the two.

gammaALPs calculates the conversion probability between photons and axions/axion-like particles in various astrophysical magnetic fields. Though focused on environments relevant to mixing between gamma rays and ALPs, this suite, written in Python, can also be used for broader applications. The code also implements various models of astrophysical magnetic fields, which can be useful for applications beyond ALP searches.

A super lightweight interface in Python to load spectra from the Pickles 1998 (stellar) and Brown 2014 (galactic) spectral catalogues

A python package created around Eric Gendron’s code for analytically (and quickly) generating field-varying SCAO PSFs for the ELT.

A reference database for astronomical instrument and telescope characteristics for all types of visual and infrared systems. Instrument packages are used in conjunction with the ScopeSim instrument data simulator.

Templates and helper functions for creating on-sky Source description objects for the ScopeSim instrument data simulation engine.

An attempt at creating a common pythonic framework for visual and infrared telescope instrument data simulators.

SORA optimally analyzes stellar occultation data. The library includes processes starting on the prediction of such events to the resulting size, shape and position of the Solar System object and can be used to build pipelines to analyze stellar occultation data. A stellar occultation is defined by the occulting body (Body), the occulted star (Star), and the time of the occultation. On the other hand, each observational station (Observer) will be associated with their light curve (LightCurve). SORA has tasks that allow the user to determine the immersion and emersion times and project them to the tangent sky plane, using the information within the Observer, Body and Star Objects. That projection will lead to chords that will be used to obtain the object’s apparent size, shape and position at the moment of the occultation. Automatic processes optimize the reduction of typical events. However, users have full control over the parameters and methods and can make changes in every step of the process.

iminuit is a Jupyter-friendly Python interface for the Minuit2 C++ library maintained by CERN's ROOT team. It can be used as a general robust function minimization method, but is most commonly used for likelihood fits of models to data, and to get model parameter error estimates from likelihood profile analysis.

CMC-COSMIC models dense star clusters using Hénon's method using orbit-averaging collisional stellar dynamics. It includes all the relevant physics for modeling dense spherical star clusters, such as strong dynamical encounters, single and binary stellar evolution, central massive black holes, three-body binary formation, and relativistic dynamics, among others. CMC is parallelized using the Message Passing Interface (MPI), and is pinned to the COSMIC (ascl:2108.022) package for binary population synthesis, which itself was originally based on the version of BSE (ascl:1303.014). COSMIC is currently a submodule within CMC, ensuring that any cluster simulations or binary populations are integrated with the same physics.

COSMIC (Compact Object Synthesis and Monte Carlo Investigation Code) generates synthetic populations with an adaptive size based on how the shape of binary parameter distributions change as the number of simulated binaries increases. It implements stellar evolution using SSE (ascl:1303.015) and binary interactions using BSE (ascl:1303.014). COSMIC can also be used to simulate a single binary at a time, a list of multiple binaries, a grid of binaries, or a fixed population size as well as restart binaries at a mid point in their evolution. The code is included in CMC-COSMIC (ascl:2108.023).

ExoPlaSim extends the PlaSim (ascl:2107.019) 3D general climate model to terrestrial exoplanets. It includes the PlaSim general circulation model and modifications that allow this code to run tidally-locked planets, planets with substantially different surface pressures than Earth, planets orbiting stars with different effective temperatures, super-Earths, and more. ExoPlaSim includes the ability to compute carbon-silicate weathering, dynamic orography through the glacier module (though only accumulation and ablation/evaporation/melting are included; glacial flow and spreading are not), and storm climatology.

DBSP_DRP reduces data from the Palomar spectrograph DBSP. Built on top of PypeIt (ascl:1911.004), it automates the reduction, fluxing, telluric correction, and combining of the red and blue sides of one night's data. The pipeline also provides several GUIs for easier control of the reduction, with one for selecting which data to reduce, and verifying the correctness of FITS headers in an editable table. Another GUI manually places traces for a sort of manually "forced" spectroscopy with the -m option, and after manually placing traces, manually selects sky regions and tweaks the FWHM of the manual traces.

PIPS analyzes the lightcurves of astronomical objects whose brightness changes periodically. Originally developed to determine the periods of RR Lyrae variable stars, the code offers many features designed for variable star analysis and can obtain period values for almost any type of lightcurve with both speed and accuracy. PIPS determines periods through several different methods, analyzes the morphology of lightcurves via Fourier analysis, estimates the statistical significance of the detected signal, and determines stellar properties based on pre-existing stellar models.

Cosmic-CoNN detects cosmic rays (CR) in CCD-captured astronomical images. It offers a PyTorch deep-learning framework to train generic, robust CR detection models for ground- and space-based imaging data as well as spectroscopic observations. Cosmic-CoNN also includes a suite of tools, including console commands, a web app, and Python APIs, to make deep-learning models easily accessible.

AutoProf performs basic and advanced non-parametric galaxy image analysis. The pipeline's design allows for fast startup and easy implementation; the package offers a suite of robust default and optional tools for surface brightness profile extractions and related methods. AUTOPROF is highly extensible and can be adapted for a variety of applications, providing flexibility for exploring new ideas and supporting advanced users.

The neural network-based emulator Chemulator advances the gas temperature and chemical abundances of a single position in an astrophysical gas. It is accurate on a single timestep and stable over many iterations with decreased accuracy, though performs less well at low visual extinctions. The code is useful for applications such as large scale ISM modeling; by retraining the emulator for a given parameter space, Chemulator could also perform more specialized applications such as planetary atmosphere modeling.

ELISa models light curves of close eclipsing binaries. It models surfaces of detached, semi-detached, and over-contact binaries, generates light curves, and generates stellar spots with given longitude, latitude, radius, and temperature. It can also fit radial velocity curves and light curves via the implementation of the non-linear least squares method and also via Markov Chain Monte Carlo method.

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.

AMOEBA (Automated Molecular Excitation Bayesian line-fitting Algorithm) employs a Bayesian approach to Gaussian decomposition, resulting in an objective and statistically robust identification of individual clouds along the line-of-sight. It uses the Python implementation of Goodman & Weare's Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler emcee (ascl:1303.002) to sample the posterior probability distribution and numerically evaluate the integrals required to compute the Bayes Factor. Amoeba takes as input a set of OH optical depth spectra and a set of expected brightness temperature spectra that are obtained by measuring the brightness temperature towards the bright background continuum source (the "on-source" observations), and in a pattern surrounding the continuum source (the "off-source" observations). Amoeba can also take as input a set of OH optical depth spectra only, and also allows input of an arbitrary number of spectra to be fit simultaneously.

The NRDD_constraints tool provides simple interpolating functions written in Python that return the most constraining limit on the dark matter-nucleon scattering cross section for a list of non-relativistic effective operators. The package contains four files: the main code, NRDD_constraints.py; a simple driver, NRDD_constraints-example.py; and two data files, NRDD_data1.npy and NRDD_data2.npy

Spectra-Without-Windows (formerly called BOSS-Without-Windows) analyzes Baryon Oscillation Spectroscopic Survey (BOSS) DR12 data using quadratic and cubic estimators. It contains analysis codes to estimate unwindowed power spectra and unwindowed bispectra. It also supplies the raw power and bispectrum spectrum measurements of BOSS and 999 Patchy simulations, and contains a utility function to generate the background number density, n(r) from the survey mask and n(z) distribution. This code has been replaced by the newer and more powerful 3D polyspectrum code PolyBin3D (ascl:2404.006).

FIREFLY (Fitting IteRativEly For Likelihood analYsis) derives stellar population properties of stellar systems, whether observed galaxy or star cluster spectra or model spectra from simulations. The code fits combinations of single-burst stellar population models to spectroscopic data following an iterative best-fitting process controlled by the Bayesian Information Criterion without applying priors. Solutions within a statistical cut are retained with their weight, which is arbitrary. No additive or multiplicative polynomia are used to adjust the spectral shape and no regularization is imposed. This fitting freedom allows mapping of the effect of intrinsic spectral energy distribution (SED) degeneracies, such as age, metallicity, dust reddening on stellar population properties, and quantifying the effect of varying input model components on such properties.

caesar-rest is a REST-ful web service for astronomical source extraction and classification with the caesar source extractor [ascl:1807.015]. The software is developed in python and consists of containerized microservices, deployable on standalone servers or on a distributed cloud infrastructure. The core component is the REST web application, based on the Flask framework and providing APIs for managing the input data (e.g. data upload/download/removal) and source finding jobs (e.g. submit, get status, get outputs) with different job management systems (Kubernetes, Slurm, Celery). Additional services (AAI, user DB, log storage, job monitor, accounting) enable the user authentication, the storage and retrieval of user data and job information, the monitoring of submitted jobs, and the aggregation of service logs and user data/job stats.

CatBoost is a machine learning method based on gradient boosting over decision trees and can be used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. It supports both numerical and categorical features and computation on CPU and GPU, and is fast and scalable. Visualization tools are also included in CatBoost.

catwoman models asymmetric transit lightcurves. Written in Python, it calculates light curves for any radially symmetric stellar limb darkening law, and where planets are modeled as two semi-circles of different radii. Catwoman is built on the batman library (ascl:1510.002) and uses its integration algorithm.