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.

The Lomb-Scargle periodogram is a common tool in the frequency analysis of unequally spaced data equivalent to least-squares fitting of sine waves. GLS is a solution for the generalization to a full sine wave fit, including an offset and weights (χ2 fitting). Compared to the Lomb-Scargle periodogram, GLS is superior as it provides more accurate frequencies, is less susceptible to aliasing, and gives a much better determination of the spectral intensity.

Glue, written in Python, links visualizations of scientific datasets across many files, allowing for interactive, linked statistical graphics of multiple files. It supports many file formats including common image formats (jpg, tiff, png), ASCII tables, astronomical image and table formats (FITS, VOT, IPAC), and HDF5. Custom data loaders can also be easily added. Glue is highly scriptable and extendable.

GMCALab solves Blind Source Separation (BSS) problems from multichannel/multispectral/hyperspectral data. In essence, multichannel data provide different observations of the same physical phenomena (e.g. multiple wavelengths), which are modeled as a linear combination of unknown elementary components or sources. Written as a set of Matlab toolboxes, it provides a generic framework that can be extended to tackle different matrix factorization problems.

GMM (Gaussian Mixture Modeling) tests the existence of bimodality in globular cluster color distributions. GMM uses three indicators to distinguish unimodal and bimodal distributions: the kurtosis of the distribution, the separation of the peaks, and the probability of obtaining the same χ2 from a unimodal distribution.

gnm is an implementation of the affine-invariant sampler for Markov chain Monte Carlo (MCMC) that uses the Gauss-Newton-Metropolis (GNM) Algorithm. The GNM algorithm is specialized in sampling highly non-linear posterior probability distribution functions of the form exp(-||f(x)||^2/2). The code includes dynamic hyper-parameter optimization to increase performance of the sampling; other features include the Jacobian tester and an error bars creator.

Gnuastro (GNU Astronomy Utilities) manipulates and analyzes astronomical data. It is an official GNU package of a large collection of programs and C/C++ library functions. Command-line programs perform arithmetic operations on images, convert FITS images to common types like JPG or PDF, convolve an image with a given kernel or matching of kernels, perform cosmological calculations, crop parts of large images (possibly in multiple files), manipulate FITS extensions and keywords, and perform statistical operations. In addition, it contains programs to make catalogs from detection maps, add noise, make mock profiles with a variety of radial functions using monte-carlo integration for their centers, match catalogs, and detect objects in an image among many other operations. The command-line programs share the same basic command-line user interface for the comfort of both the users and developers. Gnuastro is written to comply fully with the GNU coding standards and integrates well with all Unix-like operating systems. This enables astronomers to expect a fully familiar experience in the source code, building, installing and command-line user interaction that they have seen in all the other GNU software that they use. Gnuastro's extensive library is included for users who want to build their own unique programs.

The GNU Radio toolkit provides signal processing blocks to implement software radios. A software radio performs signal processing in software instead of using dedicated integrated circuits in hardware. The benefit is that since software can be easily replaced in the radio system, the same hardware can be used to create many kinds of radios for many different communications standards. GNU Radio can be used with readily-available low-cost external RF hardware to create software-defined radios and to simulate wireless communications.

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

Gollum performs spectral visualization and analysis. It offers both a programmatic interface and a visual interface that help users analyze stellar and substellar spectra, with support included for a set of precomputed synthetic spectral model grids.

GOSSIP fits the electro-magnetic emission of an object (the SED, Spectral Energy Distribution) against synthetic models to find the simulated one that best reproduces the observed data. It builds-up the observed SED of an object (or a large sample of objects) combining magnitudes in different bands and eventually a spectrum; then it performs a chi-square minimization fitting procedure versus a set of synthetic models. The fitting results are used to estimate a number of physical parameters like the Star Formation History, absolute magnitudes, stellar mass and their Probability Distribution Functions.

gotetra uses phase-space tesselation techniques to extract information about cosmological N-body simulations. The key applications of this Go-based code are the measurement of splashback shells around halos and the generation of high resolution images of density fields. The package includes routines to generates 3D and 2D (projected) density fields from a particle snapshot generated by a cosmological N-body simulation, measure density along lines of sight from the center of halos, and compresse the position space data from cosmological N-body simulations. Included are two helper libraries with functions for calculating cosmological quantities and computing a number of useful mathematical functions.

GOTHIC (Graph-bOosTed iterated HIll Climbing) detects whether a given image of a galaxy has characteristic features of a double nuclei galaxy (DNG). Galaxy interactions and mergers play a crucial role in the hierarchical growth of structure in the universe; galaxy mergers can lead to the formation of elliptical galaxies and larger disk galaxies, as well as drive galaxy evolution through star formation and nuclear activity. During mergers, the nuclei of the individual galaxies come closer and finally form a double nuclei galaxy. Although mergers are common, the detection of double-nuclei galaxies (DNGs) is rare and fairly serendipitous. Their properties can help us understand the formation of supermassive black hole (SMBH) binaries, dual active galactic nuclei (DAGN) and the associated feedback effects. GOTHIC provides an automatic and systematic way to survey data for the discovery of double nuclei galaxies.

The two-point correlation function is a simple statistic that quantifies the clustering of a given distribution of objects. In studies of the large scale structure of the Universe, it is an important tool containing information about the matter clustering and the evolution of the Universe at different cosmological epochs. A classical application of this statistic is the galaxy-galaxy correlation function to find constraints on the parameter Omega_m or the location of the baryonic acoustic oscillation peak. This calculation, however, is very expensive in terms of computer power and Graphics Processing Units provide one solution for efficient analysis of the increasingly larger galaxy surveys that are currently taking place.

GP2PCF is a public code in CUDA for performing this computation; with a single GPU board it is possible to achieve 120-fold speedups with respect to a standard implementation in C running on a single CPU. With respect to other solutions such as k-trees the improvement is of a factor of a few retaining full precision. The speedup is comparable to running in parallel in a cluster of O(100) cores.

The University of Manchester GPC library is a flexible and highly robust polygon set operations library for use with C, C#, Delphi, Java, Perl, Python, Haskell, Lua, VB.Net and other applications. It supports difference, intersection, exclusive-or and union clip operations, and polygons may be comprised of multiple disjoint contours. Contour vertices may be given in any order - clockwise or anticlockwise, and contours may be convex, concave or self-intersecting, and may be nested (i.e. polygons may have holes). Output may take the form of either polygon contours or tristrips, and hole and external contours are differentiated in the result. GPC is free for non-profit and educational use; a Commercial Use License is required for commercial use.

Internet Archive link provided for archival purposes; per its website, GPC is no longer distributed or available as of August 2020.

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

Gaussian Process Cross-Correlation (GPCC) uses Gaussian processes to estimate time delays for reverberation mapping (RM) of Active Galactic Nuclei (AGN). This statistically principled model delivers a posterior distribution for the delay and accounts for observational noise and the non-uniform sampling of the light curves. Written in Julia, GPCC quantifies the uncertainty and propagates it to subsequent calculations of dependent physical quantities, such as black hole masses. The code delivers out-of-sample predictions, which enables model selection, and can calculate the joint posterior delay for more than two light curves. Though written for RM, the software can also be applied to other fields where cross-correlation analysis is performed.

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.

Written in Python, gPhoton calibrates and sky-projects the ~1.1 trillion ultraviolet photon events detected by the microchannel plates on the Galaxy Evolution Explorer Spacecraft (GALEX), archives these events in a publicly accessible database at the Mikulski Archive for Space Telescopes (MAST), and provides tools for working with the database to extract scientific results, particularly over short time domains. The software includes a re-implementation of core functionality of the GALEX mission calibration pipeline to produce photon list files from raw spacecraft data as well as a suite of command line tools to generate calibrated light curves, images, and movies from the MAST database.

The GPI data pipeline allows users to reduce and calibrate raw GPI data into spectral and polarimetric datacubes, and to apply various PSF subtraction methods to those data. Written in IDL and available in a compiled version, the software includes an integrated calibration database to manage reference files and an interactive data viewer customized for high contrast imaging that allows exploration and manipulation of data.

GProtation measures stellar rotation periods with Gaussian processes.

This code is no longer being maintained. Please consider using celerite (ascl:1709.008) or exoplanet (ascl:1910.005) instead.

GPry efficiently obtains marginal quantities from computationally expensive likelihoods. It works best with smooth (continuous) likelihoods and posteriors that are slow to converge by other methods, which is dependent on the number of dimensions and expected shape of the posterior distribution. The likelihood should be low-dimensional (d<20 as a rule of thumb), though the code may still provide considerable improvements in speed in higher dimensions, despite an increase in the computational overhead of the algorithm. GPry is an alternative to samplers such as MCMC and Nested Sampling with a goal of speeding up inference in cosmology, though the software will work with any likelihood that can be called as a python function. It uses Cobaya's (ascl:1910.019) model framework so all of Cobaya's inbuilt likelihoods work, too.

GPS (Genesis Population Synthesis) develops population synthesis models. The code suite uses the Genesis database of planet formation models for small exoplanets (super-Earths and Mini-Neptunes). Although the codebase focuses on the Genesis models, aother models can easily be integrated with GPS. It computes the bulk compositions of the planets and simulates atmospheric loss and evolution to find the final states of the planets that can be observationally verified. GPS also offers tools to process and analyze the data from recent observations of small exoplanets in order to compare them with the models.

GPU-D is a GPU-accelerated implementation of the inverse ray-shooting technique used to generate cosmological microlensing magnification maps. These maps approximate the source plane magnification patterns created by an ensemble of stellar-mass compact objects within a foreground macrolens galaxy. Unlike other implementations, GPU-D solves the gravitational lens equation without any approximation. Due to the high computational intensity and high degree of parallelization inherent in the algorithm, it is ideal for brute-force implementation on GPUs. GPU-D uses CUDA for GPU acceleration and require NVIDIA devices to run.

GPUniverse models quantum fields in finite dimensional Hilbert spaces with Generalised Pauli Operators (GPOs) and overlapping degrees of freedom. In addition, the package can simulate sets of qubits that are only quasi independent (i.e., the Pauli algebras of different qubits have small, but non-zero anti-commutator), which is useful for validating analytical results for holographic versions of the Weyl field.

The maximum entropy method (MEM) is a well known deconvolution technique in radio-interferometry. This method solves a non-linear optimization problem with an entropy regularization term. Other heuristics such as CLEAN are faster but highly user dependent. Nevertheless, MEM has the following advantages: it is unsupervised, it has a statistical basis, it has a better resolution and better image quality under certain conditions. GPUVMEM presents a high performance GPU version of non-gridding MEM.

GR1D is based on the Eulerian formulation of GR hydrodynamics (GRHD) put forth by Romero-Ibanez-Gourgoulhon and employs radial-gauge, polar-slicing coordinates in which the 3+1 equations simplify substantially. GR1D is intended for the simulation of stellar collapse to neutron stars and black holes and will also serve as a testbed for modeling technology to be incorporated in multi-D GR codes. Its GRHD part is coupled to various finite-temperature microphysical equations of state in tabulated form that we make available with GR1D.

The chemistry and radiative cooling library Grackle provides options for primordial chemistry and cooling, photo-heating and photo-ionization from UV backgrounds, and support for user-provided arrays of volumetric and specific heating rates for astrophysical simulations and models. The library provides functions to update chemistry species; solve radiative cooling and update internal energy; and calculate cooling time, temperature, pressure, and ratio of specific heats (gamma), and has interfaces for C, C++, Fortran, and Python codes.

The GRACOS (GRAvitational COSmology) code, a parallel implementation of the particle-particle/particle-mesh (P3M) algorithm for distributed memory clusters, uses a hybrid method for both computation and domain decomposition. Long-range forces are computed using a Fourier transform gravity solver on a regular mesh; the mesh is distributed across parallel processes using a static one-dimensional slab domain decomposition. Short-range forces are computed by direct summation of close pairs; particles are distributed using a dynamic domain decomposition based on a space-filling Hilbert curve. A nearly-optimal method was devised to dynamically repartition the particle distribution so as to maintain load balance even for extremely inhomogeneous mass distributions. Tests using $800^3$ simulations on a 40-processor beowulf cluster showed good load balance and scalability up to 80 processes. There are limits on scalability imposed by communication and extreme clustering which may be removed by extending the algorithm to include adaptive mesh refinement.

Gradus.jl traces geodesics and calculates observational signatures of accreting compact objects. The code requires only a specification of the non-zero metric components of a chosen spacetime in order to solve the geodesic equation and compute a wide variety of trajectories and orbits. Gradus includes algorithms for calculating physical quantities are implemented generically, so they may be used with different classes of spacetime with minimal effort.

This paper describes the generation of initial conditions for numerical simulations in cosmology with multiple levels of resolution, or multiscale simulations. We present the theory of adaptive mesh refinement of Gaussian random fields followed by the implementation and testing of a computer code package performing this refinement called GRAFIC-2.

We present a non-parametric technique to infer the projected-mass distribution of a gravitational lens system with multiple strong-lensed images. The technique involves a dynamic grid in the lens plane on which the mass distribution of the lens is approximated by a sum of basis functions, one per grid cell. We used the projected mass densities of Plummer spheres as basis functions. A genetic algorithm then determines the mass distribution of the lens by forcing images of a single source, projected back onto the source plane, to coincide as well as possible. Averaging several tens of solutions removes the random fluctuations that are introduced by the reproduction process of genomes in the genetic algorithm and highlights those features common to all solutions. Given the positions of the images and the redshifts of the sources and the lens, we show that the mass of a gravitational lens can be retrieved with an accuracy of a few percent and that, if the sources sufficiently cover the caustics, the mass distribution of the gravitational lens can also be reliably retrieved. A major advantage of the algorithm is that it makes full use of the information contained in the radial images, unlike methods that minimise the residuals of the lens equation, and is thus able to accurately reconstruct also the inner parts of the lens.

Gramsci (GRAph Made Statistics for Cosmological Information) computes the general N-point spatial correlation functions of any discrete point set embedded within an Euclidean space of ℝ^n. It uses kd-trees and graph databases to count all possible N-tuples in binned configurations within a given length scale, e.g. all pairs of points or all triplets of points with side lengths. Gramsci can run in serial, OpenMP, MPI and hybrid parallel schemes. It is useful for performing domain decomposition of input catalogs, especially if the catalogs are large or the Rmax value is too large.

GRAND-HOD (GeneRalized ANd Differentiable Halo Occupation Distribution) takes a generalized Halo Occupation Distribution (HOD) prescription as input and outputs the corresponding mock galaxy catalogs in binary files. The code is differentiable and incorporates various generalizations to the standard HOD. It is written for the Abacus simulations, but the main functionalities can be easily adapted for other halo catalogs with the appropriate properties.

GRAPUS (GRAvitational instability PopUlation Synthesis) executes population synthesis modeling of self-gravitating disc fragmentation and tidal downsizing in protostellar discs. It reads in pre-run 1D viscous disc models of self-gravitating discs and computes where fragmentation will occur and the initial fragment mass. GRAPUS then allows these fragment embryos to evolve under various forces, including quasistatic collapse of the embryo, growth and sedimentation of the dust inside the embryo, and the formation of solid cores. The software also evolves migration due to embryo-disc interactions and tidal disruption of the embryo, and can optionally determine gravitational interactions with neighboring embryos.

GRASIL (which stands for GRAphite and SILicate) computes the spectral evolution of stellar systems taking into account the effects of dust, which absorbs and scatters optical and UV photons and emits in the IR-submm region. It may be used as well to do “standard” no-dust stellar spectral synthesis. The code is very well calibrated and applied to interpret galaxies at different redshifts. GRASIL can be downloaded or run online using the GALSYNTH WEB interface.

GRASP (General-purpose Relativistic Atomic Structure Package) calculates atomic structure, including energy levels, radiative rates (A-values) and lifetimes; it is a fully relativistic code based on the jj coupling scheme. This code has been superseded by GRASP2K (ascl:1611.007).

GRASP2K is a revised and greatly expanded version of GRASP (ascl:1609.008) and is adapted for 64-bit computer architecture. It includes new angular libraries, can transform from jj- to LSJ-coupling, and coefficients of fractional parentage have been extended to j=9/2, making calculations feasible for the lanthanides and actinides. GRASP2K identifies each atomic state by the total energy and a label for the configuration state function with the largest expansion coefficient in LSJLSJ intermediate coupling.

The Julia library GRASS produces realistic stellar spectra with time-variable granulation signatures. It is based on real observations of the Sun, and does not rely on magnetohydrodynamic simulations to produce its spectra. GRASS can also compute bisectors for absorption lines or CCF profiles, and provides two methods for calculating bisectors.

GraviDy performs N-body 3D visualizations; it is a GPU, direct-summation N-body integrator based on the Hermite scheme and includes relativistic corrections for sources of gravitational radiation. The software is modular, allowing users to readily introduce new physics, and exploits available computational resources. The software can be used in parallel on multiple CPUs and GPUs, with a considerable speed-up benefit. The single-GPU version is between one and two orders of magnitude faster than the single-CPU version.

Modern applications of strong gravitational lensing require the ability to use precise and varied observational data to constrain complex lens models. Two sets of computational methods for lensing calculations are discussed. The first is a new algorithm for solving the lens equation for general mass distributions. This algorithm makes it possible to apply arbitrarily complicated models to observed lenses. The second is an evaluation of techniques for using observational data including positions, fluxes, and time delays of point-like images, as well as maps of extended images, to constrain models of strong lenses. The techniques presented here are implemented in a flexible and user-friendly software package called gravlens, which is made available to the community.

The non-parametric Jeans code GravSphere models discrete data and can be used to model dark matter distributions in galaxies. It can also recover the density ρ(r) and velocity anisotropy β(r) of spherical stellar systems, assuming only that they are in a steady state. Real or mock data are prepared by using the included binulator.py code; the repository also includes many examples for exploring the GravSphere's capabilities.

GRay is a massive parallel ordinary differential equation integrator that employs the "stream processing paradigm." It is designed to efficiently integrate billions of photons in curved spacetime according to Einstein's general theory of relativity. The code is implemented in CUDA C/C++.

GRBoondi simulates generalized Proca fields on arbitrary analytic fixed backgrounds; it is based on the publicly available 3+1D numerical relativity code GRChombo (ascl:2306.039). GRBoondi reduces the prerequisite knowledge of numerical relativity and GRChombo in the numerical studies of generalized Proca theories. The main steps to perform a study are inputting the additions to the equations of motion beyond the base Proca theory; GRBoondi can then automatically incorporate the higher-order terms in the simulation. The code is written entirely in C++14 and uses hybrid MPI/OpenMP parallelism. GRBoondi inherits all of the capabilities of the main GRChombo code, which makes use of the Chombo library (ascl:1202.008) for adaptive mesh refinement.

GRChombo performs numerical relativity simulations. It uses Chombo (ascl:1202.008) for adaptive mesh refinement and can evolve standard spacetimes such as binary black hole mergers and scalar collapses into black holes. The code supports non-trivial many-boxes-in-many-boxes mesh hierarchies and massive parallelism and evolves the Einstein equation using the standard BSSN formalism. GRChombo is written in C++14 and uses hybrid MPI/OpenMP parallelism and vector intrinsics to achieve good performance.

GRDzhadzha evolves matter on curved spacetimes with an analytic time and space dependence. Written in C++14, it uses hybrid MPI/OpenMP parallelism to achieve good performance. The code is based on publicly available 3+1D numerical relativity code GRChombo (ascl:2306.039) and inherits all of the capabilities of the main GRChombo code, which uses the Chombo library for adaptive mesh refinement.

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

GRFolres performs simulations in modified theories of gravity. It is based on GRChombo (ascl:2306.039) and inherits all of the capabilities of the main GRChombo code, which makes use of the Chombo library (ascl:1202.008) for adaptive mesh refinement. The code implements the 4∂ST theory of modified gravity and the cubic Horndeski theory in (3+1)-dimensional numerical relativity. GRFolres can be used for stable gauge evolution, solving the modified energy and momentum constraints for initial conditions, and monitoring the constraint violation and calculating the energy densities associated with the different scalar terms in the action. It can also extract data for the tensor and scalar gravitational waveforms.

GrGadget merges the Particle-Mesh (PM) relativistic GEVOLUTION code (ascl:1608.014) with the TreePM GADGET-4 code (ascl:2204.014) to create a TreePM simulation code that represents metric perturbations at the scales where they are relevant while resolving non-linear structures. The better resolution of the highly non-linear regime improves the representation of the relativistic fields sampled on the mesh with respect to PM-only simulations.

GRID-core is a core-finding method using the contours of the local gravitational potential to identify core boundaries. The GRID-core method applied to 2D surface density and 3D volume density are in good agreement for bound cores. We have implemented a version of the GRID-core algorithm in IDL, suitable for core-finding in observed maps. The required input is a two-dimensional FITS file containing a map of the column density in a region of a cloud.

GRIM (General Relativistic Implicit Magnetohydrodynamics) evolves a covariant extended magnetohydrodynamics model derived by treating non-ideal effects as a perturbation of ideal magnetohydrodynamics. Non-ideal effects are modeled through heat conduction along magnetic field lines and a difference between the pressure parallel and perpendicular to the field lines. The model relies on an effective collisionality in the disc from wave-particle scattering and velocity-space (mirror and firehose) instabilities. GRIM, which runs on CPUs as well as on GPUs, combines time evolution and primitive variable inversion needed for conservative schemes into a single step using only the residuals of the governing equations as inputs. This enables the code to be physics agnostic as well as flexible regarding time-stepping schemes.

GRINN (Gravity Informed Neural Network) solves the coupled set of time-dependent partial differential equations describing the evolution of self-gravitating flows in one, two, and three spatial dimensions. It is based on physics informed neural networks (PINNs), which are mesh-free and offer a fundamentally different approach to solving such partial differential equations. GRINN has solved for the evolution of self-gravitating, small-amplitude perturbations and long-wavelength perturbations and, when modeling 3D astrophysical flows, provides accuracy on par with finite difference (FD) codes with an improvement in computational speed.

GRIP (Generic data Reduction for nulling Interferometry Package) reduces nulling data with enhanced statistical self-calibration methods from any nulling interferometric instrument within a single and consistent framework. The toolbox self-calibrates null depth measurements by fitting a model of the instrumental perturbations to histograms of data. The model is generated using a simulator of the instrument built into the package for the main operating nullers or provided by the user. GRIP handles baseline discrimination and spectral dispersion and features several optimizing strategy, including least squares, maximum likelihood, and MCMC with emcee (ascl:1303.002), and works on GPU using the cupy library.

GriSPy (Grid Search in Python) uses a regular grid search algorithm for quick fixed-radius nearest-neighbor lookup. It indexes a set of k-dimensional points in a regular grid providing a fast approach for nearest neighbors queries. Optional periodic boundary conditions can be provided for each axis individually. GriSPy implements three types of queries: bubble, shell and the nth-nearest, and offers three different metrics of interest in astronomy: the Euclidean and two distance functions in spherical coordinates of varying precision, haversine and Vincenty. It also provides a custom distance function. GriSPy is particularly useful for large datasets where a brute-force search is not practical.

GRIT (Gravitational Rigid-body InTegrators) simulaties the coupled dynamics of both spin and orbit of N gravitationally interacting rigid bodies. The code supports tidal forces and general relativity correction are supported, and multiple schemes with different orders of convergences and splitting strategies are available. Multiscale splittings boost the simulation speed, and force evaluations can be parallelized. In addition, each body can be set to be a rigid body or just a point mass, and the floating-point format can be customized as float, double, or long double globally.

Grizli produces quantitative and comprehensive modeling and fitting of slitless spectroscopic observations, which typically involve overlapping spectra of hundreds or thousands of objects in exposures taken with one or more separate grisms and at multiple dispersion position angles. This type of analysis provides complete and uniform characterization of the spectral properties (e.g., continuum shape, redshifts, line fluxes) of all objects in a given exposure taken in the slitless spectroscopic mode.

GRIZZLY simulates reionization using a 1D radiative transfer scheme. The code enables the efficient exploration of the parameter space for evaluating 21cm brightness temperature fluctuations near the cosmic dawn. GRIZZLY builds upon the BEARS algorithm for generating simulated reionization maps with density and velocity fields, which are useful for profiling dark matter halos and cosmological density fields.

grmonty is a Monte Carlo radiative transport code intended for calculating spectra of hot, optically thin plasmas in full general relativity. The code models hot accretion flows in the Kerr metric, it incorporates synchrotron emission and absorption and Compton scattering. grmonty can be readily generalized to account for other radiative processes and an arbitrary spacetime.

Growl calculates the linear growth factor Da and its logarithmic derivative dln D/dln a in expanding Friedmann-Robertson-Walker universes with arbitrary matter and vacuum densities. It permits rapid and stable numerical evaluation.

grtrans calculates ray tracing radiative transfer in the Kerr metric, including the full treatment of polarised radiative transfer and parallel transport along geodesics, for comparing theoretical models of black hole accretion flows and jets with observations. The code is written in Fortran 90 and parallelizes with OpenMP; the full code and several components have Python interfaces. grtrans includes Geokerr (ascl:1011.015) and requires cfitsio (ascl:1010.001) and pyfits (ascl:1207.009).

GRUMPY (Galaxy formation with RegUlator Model in PYthon) models the formation of dwarf galaxies. When coupled with realistic mass accretion histories of halos from simulations and reasonable choices for model parameter values, this simple regulator-type framework reproduces a broad range of observed properties of dwarf galaxies over seven orders of magnitude in stellar mass. GRUMPY matches observational constraints on the stellar mass--halo mass relation and observed relations between stellar mass and gas phase and stellar metallicities, gas mass, size, and star formation rate. It also models the general form and diversity of star formation histories (SFHs) of observed dwarf galaxies. The software can be used to predict photometric properties of dwarf galaxies hosted by dark matter haloes in N-body simulations, such as colors, surface brightnesses, and mass-to-light ratios and to forward model observations of dwarf galaxies.

The GSD library reads data written in the James Clerk Maxwell Telescope GSD format. This format uses the General Single-Dish Data model and was used at the JCMT until 2005. The library provides an API to open GSD files and read their contents. The content of the data files is self-describing and the library can return the type and name of any component. The library is used by SPECX (ascl:1310.008), JCMTDR (ascl:1406.019) and COADD (ascl:1411.020). The SMURF (ascl:1310.007) package can convert GSD heterodyne data files to ACSIS format using this library.

gsf applies Gaussian Mixture Models in the stellar kinematic space of normalized angular momentum and binding energy on NIHAO high resolution galaxies to separate the stars into multiple components. The gsf analysis package assumes that the simulation snapshot has been pre-processed with a halo finder. It is based on pynbody (ascl:1305.002) and the scikit-learnpython package for Machine Learning; after loading, orienting, and transforming a simulation snapshot to physical units, it runs the clustering algorithm and performs the direct N-body gravity force using all the particles in the given halo.

gsf fits photometric data points, simultaneously with grism spectra if provided, to get posterior probability of galaxy physical properties, such as stellar mass, dust attenuation, metallicity, as well as star formation and metallicity enrichment histories. Designed for extra-galactic science, this flexible, python-based SED fitting code involves a Markov-Chain Monte-Carlo (MCMC) process, and may take more time (depending on the number of parameters and length of MCMC chains) than other SED fitting codes based on chi-square minimization.

GSGS does phase retrieval on images given an estimate of the pupil phase (from a non-redundant mask or other interferometric approach), the pupil geometry, and the in-focus image. The code uses a modified Gerchberg-Saxton algorithm that iterates between pupil plane and image plane to measure the pupil phase.

GSpec analyzes the Fermi mission's Gamma-ray Burst Monitor (GBM) data via a user-interactive GUI. The software provides a seamless interface to XSPEC (ascl:9910.005). It allows users to create their own Python scripts using the included libraries, and to define additional data reduction techniques, such as background fitting/estimation and data binning, as Python-based plugins. It is part of a larger effort to produce a set of GBM data tools to allow the broader community to analyze all aspects of GBM data, including the continuous data that GBM produces. GSpec is similar to RMfit (ascl:1409.011), a GUI-based spectral analysis code that specializes in the analysis of GBM trigger data, and is intended to eventually replace that IDL package.

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.

Guacho is a 3D hydrodynamical/magnetohydrodynamical code suited for astrophysical fluids. The hydrodynamic equations are evolved with a number of approximate Riemann solvers. Gaucho includes various modules to deal with different cooling regimes, and a radiation transfer module based on a Monte Carlo ray tracing method. The code can run sequentially or in parallel with MPI.

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

The gw_pta_emulator reads in gravitational wave (GW) characteristic strain spectra from black-hole population simulations, re-bins for the user's observing baseline, and constructs new spectra. The user can train a Gaussian process to emulate the spectral behavior at all frequencies across the astrophysical parameter space of supermassive black-hole binary environments.

GWDALI focuses on parameter estimations of gravitational waves generated by compact object coalescence (CBC). This software employs both Gaussian (Fisher Matrix) and Beyond-Gaussian methods to approximate the likelihood of gravitational wave events. GWDALI also addresses the challenges posed by Fisher Matrices with zero determinants. Additionally, the Beyond-Gaussian approach incorporates the Derivative Approximation for Likelihoods (DALI) algorithm, enabling a more reliable estimation process.

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.

GWecc computes the pulsar timing array (PTA) signals induced by eccentric supermassive binaries. Written in C++, it computes the plus/cross polarizations as well as Earth and pulsar terms of the PTA signal given the binary parameters and the sky locations of the binary and the pulsar. A python wrapper is included through which GWecc can be used to simulate, search for and constrain gravitational wave-emitting eccentric supermassive binaries using packages such as ENTERPRISE (ascl:1912.015) and libstempo (ascl:2002.017).

GWecc is superseded by GWecc.jl and is no longer maintained.

GWFAST forecasts the signal-to-noise ratios and parameter estimation capabilities of networks of gravitational-wave detectors, based on the Fisher information matrix approximation. It is designed for applications to third-generation gravitational-wave detectors. It is based on Automatic Differentiation, which makes use of the library JAX (ascl:2111.002). This allows efficient parallelization and numerical accuracy. The code includes a module for parallel computation on clusters.

gwforge generates mock gravitational wave detector data using user-defined population and arbitrary detector sensitivity. The code can, for example, simulate a wide range of binary source populations by specifying parameters such as the local merger rate, distribution functions, and additional keyword arguments, and simulate coloured Gaussian or zero noise using a provided or default power spectrum to represent the detector noise. gwforge can also inject gravitational wave signal(s) into the generated detector data using the previously generated population and a chosen waveform model.

GWFrames eliminates all rotational behavior, thus simplifying the waveform as much as possible and allowing direct generalizations of methods for analyzing nonprecessing systems. In the process, the angular velocity of a waveform is introduced, which also has important uses, such as supplying a partial solution to an important inverse problem.

This code is no longer maintained; much of its functionality has been moved to scri (ascl:2303.011) or to sxs.

The Python package GWpy analyzes and characterizes gravitational wave data. It provides a user-friendly, intuitive interface to the common time-domain and frequency-domain data produced by the LIGO and Virgo observatories and their analyses. The core Python infrastructure is influenced by, and extends the functionality of, the Astropy (ascl:1304.002) package, and its methodology has been derived from, and augmented by, the LIGO Algorithm Library Suite (LALSuite), a large collection of primarily C99 routines for analysis and manipulation of data from gravitational-wave detectors. These packages use the SWIG program to produce Python wrappings for all C modules, allowing the GWpy package to leverage both the completeness, and the speed, of these libraries.

GWSim generates mock gravitational waves (GW) events corresponding to different binary black holes (BBHs) population models. It can incorporate scenarios of GW mass models, GW spin distributions, the merger rate, and the cosmological parameters. GWSim generates samples of binary compact objects for a fixed amount of observation time, duty cycle, and configurations of the detector network; the universe created by the code is uniform in comobile volume.

GWSurrogate provides an easy to use interface to gravitational wave surrogate models. Surrogates provide a fast and accurate evaluation mechanism for gravitational waveforms which would otherwise be found through solving differential equations. These equations must be solved in the “building” phase, which was performed using other codes.

GWToolbox simulates gravitational wave observations for various detectors. The package is composed of three modules, namely the ground-based detectors (and their targets), the space-borne detectors (and their targets) and pulsar timing arrays (PTA). These three modules work independently and have different dependencies on other packages and libraries; failed dependencies met in one module will not influence the usage of another module. GWToolbox can accessed with a web interface (gw-universe.org) or as a python package (https://bitbucket.org/radboudradiolab/gwtoolbox).

GYOTO, a general relativistic ray-tracing code, aims at computing images of astronomical bodies in the vicinity of compact objects, as well as trajectories of massive bodies in relativistic environments. This code is capable of integrating the null and timelike geodesic equations not only in the Kerr metric, but also in any metric computed numerically within the 3+1 formalism of general relativity. Simulated images and spectra have been computed for a variety of astronomical targets, such as a moving star or a toroidal accretion structure. The underlying code is open source and freely available. It is user-friendly, quickly handled and very modular so that extensions are easy to integrate. Custom analytical metrics and astronomical targets can be implemented in C++ plug-in extensions independent from the main code.

GYRE is an oscillation code that solves the stellar pulsation equations (both adiabatic and non-adiabatic) using a novel Magnus Multiple Shooting numerical scheme devised to overcome certain weaknesses of the usual relaxation and shooting schemes. The code is accurate (up to 6th order in the number of grid points), robust, and makes efficient use of multiple processor cores and/or nodes.

gyrfalcON (GalaxY simulatoR using falcON) is a full-fledged N-body code using Dehnen’s force algorithm of complexity O(N) (falcON); this algorithm is approximately 10 times faster than an optimally coded tree code. The code features individual adaptive time steps and individual (but fixed) softening lengths. gyrfalcON is included in and requires NEMO (ascl:1010.051) to run.

gyrointerp calculates gyrochronal ages by interpolating between open cluster rotation sequences. The framework, written in Python, can be used to find the gyrochronological age posterior of single or many stars. It can also produce a visual interpolation for a star’s age to determine where the star falls in the rotation-temperature plane in comparison to known reference clusters. gyrointerp models the ensemble evolution of rotation periods for main-sequence stars with temperatures of 3800-6200 K (masses of 0.5-1.2 solar) and is not applicable for subgiant or giant stars, and should be used cautiously with binary stars, as they can observationally bias temperature and rotation period measurements.

H-FISTA (Hierarchical Fast Iterative Shrinkage Thresholding Algorithm) retrieves the phases of the wavefield from intensity measurements for pulsar spectroscopy. The code accepts input data in ASCII format as produced by PSRchive's (ascl:1105.014) psrflux function, a FITS file, or a pickle. If using a notebook, any custom reader can be used as long as the data ends up in a NumPy array. H-FISTA obtains sparse models of the wavefield in a hierarchical approach with progressively increasing depth. Once the tail of the noise distribution is reached, the hierarchy terminates with a final unregularized optimization, resulting in a fully dense model of the complex wavefield that permits the discovery of faint signals by appropriate averaging.

HADES analyzse dust levels in simulated CMB galactic dust maps with realistic experimental noise and lensing configurations. It allows detection of dust via its anisotropy properties in CMB B-modes. It also includes techniques for computing null-tests and a rudimentary technique for dedusting.

HAFFET (Hybrid Analytic Flux FittEr for Transients) analyzes supernovae photometric and spectroscopic data. It handles observational data for a set of targets, estimates their physical parameters, and visualizes the population of inferred parameters. HAFFET defines two classes, snobject for data and fittings for one specific object, and snelist to organize the overall running for a list of objects. The HAFFET package includes utilities for downloading SN data from online sources, intepolating multi band lightcurves, characterizing the first light and rising of SNe with power law fits, and matching epochs of different bands. It can also calculate colors, and/or construct the spectral energy distribution (SED), estimate bolometric LCs and host galaxy extinction, fit the constructed bolometric lightcurves to different models, and identify and fit the absorption minima of spectral lines, in addition to performing other tasks. In addition to utilizing the built-in models, users can add their own models or import models from other python packages.

HaloAnalysis reads and analyzes halo/galaxy catalogs, generated from Rockstar (ascl:1210.008) or AHF (ascl:1102.009), and merger trees generated from Consistent Trees (ascl:1210.011). Written in Python, it offers the following functionalities: reads halo/galaxy/tree catalogs from multiple file formats; assigns baryonic particles and galaxy properties to dark-matter halos; combines and re-generates halo/galaxy/tree files in hdf5 format; analyzes properties of halos/galaxies; and selects halos to generate zoom-in initial conditions. The code includes a tutorial in the form of a Jupyter notebook.

HALOFIT provides an explanatory framework for galaxy bias and clustering and has been incorporated into CMB packages such as CMBFAST (ascl:9909.004) and CAMB (ascl:1102.026). It attains a reasonable level of precision, though the halo model does not match N-body data perfectly. The code is written in Fortran 77. HALOFIT tends to underpredict the power on the smallest scales in standard LCDM universes (although HALOFIT was designed to work for a much wider range of power spectra); its accuracy can be improved by using a supplied correction.

We perform N-body simulations of theories with infinite-volume extra dimensions, such as the Dvali-Gabadadze-Porrati (DGP) model and its higher-dimensional generalizations, where 4D gravity is mediated by massive gravitons. The longitudinal mode of these gravitons mediates an extra scalar force, which we model as a density-dependent modification to the Poisson equation. This enhances gravitational clustering, particularly on scales that have undergone mild nonlinear processing. While the standard non-linear fitting algorithm of Smith et al. overestimates this power enhancement on non-linear scales, we present a modified fitting formula that offers a remarkably good fit to our power spectra. Due to the uncertainty in galaxy bias, our results are consistent with precision power spectrum determinations from galaxy redshift surveys, even for graviton Compton wavelengths as small as 300 Mpc. Our model is sufficiently general that we expect it to capture the phenomenology of a wide class of related higher-dimensional gravity scenarios.

HaloFlow uses a machine learning approach to infer Mh and stellar mass, M∗, using grizy band magnitudes, morphological properties quantifying characteristic size, concentration, and asymmetry, total measured satellite luminosity, and number of satellites.

HALOGEN generates approximate synthetic halo catalogs. Written in C, it decomposes the problem of generating cosmological tracer distributions (eg. halos) into four steps: generating an approximate density field, generating the required number of tracers from a CDF over mass, placing the tracers on field particles according to a bias scheme dependent on local density, and assigning velocities to the tracers based on velocities of local particles. It also implements a default set of four models for these steps. HALOGEN uses 2LPTic (ascl:1201.005) and CUTE (ascl:1505.016); the software is flexible and can be adapted to varying cosmologies and simulation specifications.

HaloGen computes all auto and cross spectra and halo model trispectrum in simple configurations. This modular halo model code computes 3d power spectra, and the corresponding projected 2d power spectra in the Limber and flat sky approximations. The observables include matter density, galaxy lensing, CMB lensing, thermal Sunyaev-Zel'dovich, cosmic infrared background, tracers with any dn/dz, b(z) and HOD.

Halogen, written in C, generates multimass spherically symmetric initial conditions for N-body simulations. A large family of radial density profiles is supported. The initial conditions are sampled from the full distribution function.

HaloGraphNet predicts halo masses from simulations using Graph Neural Networks. Given a dark matter halo and its galaxies, this software creates a graph with information about the 3D position, stellar mass and other properties. It then trains a Graph Neural Network to predict the mass of the host halo. Data are taken from the CAMELS hydrodynamic simulations.

halomod calculates cosmological halo model and HOD quantities. It is built on HMF (ascl:1412.006); it retains that code's features and provides extended components for the halo model, including numerous halo bias models, including scale-dependent bias, basic concentration-mass-redshift relations, and several plug-and-play halo-exclusion models. halomod includes built-in HOD parameterizations and halo profiles, support for WDM models, and all basic quantities such as 3D correlations and power spectra, and also several derived quantities such as effective bias and satellite fraction. In addition, it offers a simple routine for populating a halo catalog with galaxies via a HOD. halomod is flexible and modular, making it easily extendable.

Halotools builds and tests models of the galaxy-halo connection and analyzes catalogs of dark matter halos. The core functions of the package include fast generation of synthetic galaxy populations using HODs, abundance matching, and related methods; efficient algorithms for calculating galaxy clustering, lensing, z-space distortions, and other astronomical statistics; a modular, object-oriented framework for designing galaxy evolution models; and end-to-end support for reducing halo catalogs and caching them as hdf5 files.

HAM solves non-relativistic hyperbolic partial differential equations in conservative form using high-resolution shock-capturing techniques. This version of HAM has been configured to solve the magnetohydrodynamic equations of motion in axisymmetry to evolve a shearing box model.

The Hammurabi code is a publicly available C++ code for generating mock polarized observations of Galactic synchrotron emission with telescopes such as LOFAR, SKA, Planck, and WMAP, based on model inputs for the Galactic magnetic field (GMF), the cosmic-ray density distribution, and the thermal electron density. The Hammurabi code allows one to perform simulations of several different data sets simultaneously, providing a more reliable constraint of the magnetized ISM.