Results 851-900 of 2487 (2445 ASCL, 42 submitted)
GALSVM is IDL software for automated morphology classification. It was specially designed for high redshift data but can be used at low redshift as well. It analyzes morphologies of galaxies based on a particular family of learning machines called support vector machines. The method can be seen as a generalization of the classical CAS classification but with an unlimited number of dimensions and non-linear boundaries between decision regions. It is fully automated and consequently well adapted to large cosmological surveys.
GAMBIT (Global And Modular BSM Inference Tool) performs statistical global fits of generic physics models using a wide range of particle physics and astrophysics data. Modules provide native simulations of collider and astrophysics experiments, a flexible system for interfacing external codes (the backend system), a fully featured statistical and parameter scanning framework, and additional tools for implementing and using hierarchical models.
GAME infers different ISM physical properties by analyzing the emission line intensities in a galaxy spectrum. The code is trained with a large library of synthetic spectra spanning many different ISM phases, including HII (ionized) regions, PDRs, and neutral regions. GAME is based on a Supervised Machine Learning algorithm called AdaBoost with Decision Trees as base learner. Given a set of input lines in a spectrum, the code performs a training on the library and then evaluates the line intensities to give a determination of the physical properties. The errors on the input emission line intensities and the uncertainties on the physical properties determinations are also taken into account. GAME infers gas density, column density, far-ultraviolet (FUV, 6–13.6 eV) flux, ionization parameter, metallicity, escape fraction, and visual extinction. A web interface for using the code is available.
GAMER (GPU-accelerated Adaptive MEsh Refinement) serves as a general-purpose adaptive mesh refinement + GPU framework and solves hydrodynamics with self-gravity. The code supports adaptive mesh refinement (AMR), hydrodynamics with self-gravity, and a variety of GPU-accelerated hydrodynamic and Poisson solvers. It also supports hybrid OpenMP/MPI/GPU parallelization, concurrent CPU/GPU execution for performance optimization, and Hilbert space-filling curve for load balance. Although the code is designed for simulating galaxy formation, it can be easily modified to solve a variety of applications with different governing equations. All optimization strategies implemented in the code can be inherited straightforwardly.
The GammaLib is a versatile toolbox for the high-level analysis of astronomical gamma-ray data. It is implemented as a C++ library that is fully scriptable in the Python scripting language. The library provides core functionalities such as data input and output, interfaces for parameter specifications, and a reporting and logging interface. It implements instruments specific functionalities such as instrument response functions and data formats. Instrument specific functionalities share a common interface to allow for extension of the GammaLib to include new gamma-ray instruments. The GammaLib provides an abstract data analysis framework that enables simultaneous multi-mission analysis.
Gammapy analyzes gamma-ray data and creates sky images, spectra and lightcurves, from event lists and instrument response information; it can also determine the position, morphology and spectra of gamma-ray sources. It is used to analyze data from H.E.S.S., Fermi-LAT, and the Cherenkov Telescope Array (CTA).
Ganalyzer is a model-based tool that automatically analyzes and classifies galaxy images. Ganalyzer works by separating the galaxy pixels from the background pixels, finding the center and radius of the galaxy, generating the radial intensity plot, and then computing the slopes of the peaks detected in the radial intensity plot to measure the spirality of the galaxy and determine its morphological class. Unlike algorithms that are based on machine learning, Ganalyzer is based on measuring the spirality of the galaxy, a task that is difficult to perform manually, and in many cases can provide a more accurate analysis compared to manual observation. Ganalyzer is simple to use, and can be easily embedded into other image analysis applications. Another advantage is its speed, which allows it to analyze ~10,000,000 galaxy images in five days using a standard modern desktop computer. These capabilities can make Ganalyzer a useful tool in analyzing large datasets of galaxy images collected by autonomous sky surveys such as SDSS, LSST or DES.
GANDALF (Gas AND Absorption Line Fitting) accurately separates the stellar and emission-line contributions to observed spectra. The IDL code includes reddening by interstellar dust and also returns formal errors on the position, width, amplitude and flux of the emission lines. Example wrappers that make use of pPXF (ascl:1210.002) to derive the stellar kinematics are included.
GANDALF, a successor to SEREN (ascl:1102.010), is a hybrid self-gravitating fluid dynamics and collisional N-body code primarily designed for investigating star formation and planet formation problems. GANDALF uses various implementations of Smoothed Particle Hydrodynamics (SPH) to perform hydrodynamical simulations of gas clouds undergoing gravitational collapse to form new stars (or other objects), and can perform simulations of pure N-body dynamics using high accuracy N-body integrators, model the intermediate phase of cluster evolution, and provide visualizations via its python interface as well as interactive simulations. Although based on many of the SEREN routines, GANDALF has been largely re-written from scratch in C++ using more optimal algorithms and data structures.
The algorithm Gaussian processes can reconstruct a function from a sample of data without assuming a parameterization of the function. The GaPP code can be used on any dataset to reconstruct a function. It handles individual error bars on the data and can be used to determine the derivatives of the reconstructed function. The data sample can consist of observations of the function and of its first derivative.
We present a new method for detecting the missing baryons by generating a template for the kinematic Sunyaev-Zel'dovich effect. The template is computed from the product of a reconstructed velocity field with a galaxy field. We provide maps of such templates constructed from SDSS Data Release 7 spectroscopic data (SDSS VAGC sample) along side with their expected two point correlation functions with CMB temperature anisotropies. Codes of generating such coefficients of the two point correlation function are also released to provide users of the gas-momentum map a way to change the parameters such as cosmological parameters, reionization history, ionization parameters, etc.
GASGANO is a GUI software tool for managing and viewing data files produced by VLT Control System (VCS) and the Data Flow System (DFS). It is developed and maintained by ESO to help its user community manage and organize astronomical data observed and produced by all VLT compliant telescopes in a systematic way. The software understands FITS, PAF, and ASCII files, and Reduction Blocks, and can group, sort, classify, filter, and search data in addition to allowing the user to browse, view, and manage them.
Gasoline solves the equations of gravity and hydrodynamics in astrophysical problems, including simulations of planets, stars, and galaxies. It uses an SPH method that features correct mixing behavior in multiphase fluids and minimal artificial viscosity. This method is identical to the SPH method used in the ChaNGa code (ascl:1105.005), allowing users to extend results to problems requiring >100,000 cores. Gasoline uses a fast, memory-efficient O(N log N) KD-Tree to solve Poisson's Equation for gravity and avoids artificial viscosity in non-shocking compressive flows.
Gatspy contains efficient, well-documented implementations of several common routines for Astronomical time series analysis, including the Lomb-Scargle periodogram, the Supersmoother method, and others.
GAUSSCLUMPS decomposes a spectral map into Gaussian-shape clumps. The clump-finding algorithm decomposes a spectral data cube by iteratively removing 3-D Gaussians as representative clumps. GAUSSCLUMPS was originally a separate code distribution but is now a contributed package in GILDAS (ascl:1305.010). A reimplementation can also be found in CUPID (ascl:1311.007).
GaussFit solves least squares and robust estimation problems; written originally for reduction of NASA Hubble Space Telescope data, it includes a complete programming language designed especially to formulate estimation problems, a built-in compiler and interpreter to support the programming language, and a built-in algebraic manipulator for calculating the required partial derivatives analytically. The code can handle nonlinear models, exact constraints, correlated observations, and models where the equations of condition contain more than one observed quantity. Written in C, GaussFit includes an experimental robust estimation capability so data sets contaminated by outliers can be handled simply and efficiently.
GaussPy implements the Autonomous Gaussian Decomposition (AGD) algorithm, which uses computer vision and machine learning techniques to provide optimized initial guesses for the parameters of a multi-component Gaussian model automatically and efficiently. The speed and adaptability of AGD allow it to interpret large volumes of spectral data efficiently. Although it was initially designed for applications in radio astrophysics, AGD can be used to search for one-dimensional Gaussian (or any other single-peaked spectral profile)-shaped components in any data set. To determine how many Gaussian functions to include in a model and what their parameters are, AGD uses a technique called derivative spectroscopy. The derivatives of a spectrum can efficiently identify shapes within that spectrum corresponding to the underlying model, including gradients, curvature and edges.
GaussPy+ is a fully automated Gaussian decomposition package for emission line spectra. It is based on GaussPy (ascl:1907.019) and offers several improvements, including automating preparatory steps and providing an accurate noise estimation, improving the fitting routine, and providing a routine to refit spectra based on neighboring fit solutions. GaussPy+ handles complex emission and low to moderate signal-to-noise values.
GBART is an improved version of the code for determining the orbital elements for spectroscopic binaries originally written by Bertiau & Grobben (1968).
GBKFIT performs galaxy kinematic modeling. It can be used to extract morphological and kinematical properties of galaxies by fitting models to spatially resolved kinematic data. The software can also take beam smearing into account by using the knowledge of the line and point spread functions. GBKFIT can take advantage of many-core and massively parallel architectures such as multi-core CPUs and Graphics Processing Units (GPUs), making it suitable for modeling large-scale surveys of thousands of galaxies within a very seasonable time frame. GBKFIT features an extensible object-oriented architecture that supports arbitrary models and optimization techniques in the form of modules; users can write custom modules without modifying GBKFIT’s source code. The software is written in C++ and conforms to the latest ISO standards.
GBTIDL is an interactive package for reduction and analysis of spectral line data taken with the Robert C. Byrd Green Bank Telescope (GBT). The package, written entirely in IDL, consists of straightforward yet flexible calibration, averaging, and analysis procedures (the "GUIDE layer") modeled after the UniPOPS and CLASS data reduction philosophies, a customized plotter with many built-in visualization features, and Data I/O and toolbox functionality that can be used for more advanced tasks. GBTIDL makes use of data structures which can also be used to store intermediate results. The package consumes and produces data in GBT SDFITS format. GBTIDL can be run online and have access to the most recent data coming off the telescope, or can be run offline on preprocessed SDFITS files.
gdr2_completeness queries Gaia DR2 TAP services and divides the queries into sub-queries chunked into arbitrary healpix bins. Downloaded data are formatted into arrays. Internal completeness is calculated by dividing the total starcount and starcounts with an applied cut (e.g., radial velocity measurement and good parallax). Independent determination of the external GDR2 completeness per healpix (level 6) and G magnitude bin (3 coarse bins: 8-12,12-15,15-18) is inferred from a crossmatch with 2MASS data. The overall completeness of a specific GDR2 sample can be approximated by multiplying the internal with the external completeness map, which is useful when data are compared to models thereof. Jupyter notebooks showcasing both utilities enable the user to easily construct the overall completeness for arbitrary samples of the GDR2 catalogue.
Geant4 is a toolkit for simulating the passage of particles through matter. It includes a complete range of functionality including tracking, geometry, physics models and hits. The physics processes offered cover a comprehensive range, including electromagnetic, hadronic and optical processes, a large set of long-lived particles, materials and elements, over a wide energy range starting, in some cases, from 250eV and extending in others to the TeV energy range. It has been designed and constructed to expose the physics models utilised, to handle complex geometries, and to enable its easy adaptation for optimal use in different sets of applications. The toolkit is the result of a worldwide collaboration of physicists and software engineers. It has been created exploiting software engineering and object-oriented technology and implemented in the C++ programming language. It has been used in applications in particle physics, nuclear physics, accelerator design, space engineering and medical physics.
The Gemini IRAF package processes observational data obtained with the Gemini telescopes. It is an external package layered upon IRAF and supports data from numerous instruments, including FLAMINGOS-2, GMOS-N, GMOS-S, GNIRS, GSAOI, NIFS, and NIRI. The Gemini IRAF package is organized into sub-packages; it contains a generic tools package, "gemtools", along with instrument-specific packages. The raw data from the Gemini facility instruments are stored as Multi-Extension FITS (MEF) files. Therefore, all the tasks in the Gemini IRAF package, intended for processing data from the Gemini facility instruments, are capable of handling MEF files.
Gemini is a toolkit for analytical models of two-point correlations and inhomogeneous structure formation. It extends standard Press-Schechter theory to inhomogeneous situations, allowing a realistic, analytical calculation of halo correlations and bias.
This general complex polynomial root solver, implemented in Fortran and further optimized for binary microlenses, uses a new algorithm to solve polynomial equations and is 1.6-3 times faster than the ZROOTS subroutine that is commercially available from Numerical Recipes, depending on application. The largest improvement, when compared to naive solvers, comes from a fail-safe procedure that permits skipping the majority of the calculations in the great majority of cases, without risking catastrophic failure in the few cases that these are actually required.
GenetIC generates initial conditions for cosmological simulations, especially for zoom simulations of galaxies. It provides support for "genetic modifications" of specific attributes of simulations to allow study of the impact of such modifications on the outcomes; the code can also produce constrained initial conditions.
GENGA (Gravitational ENcounters with Gpu Acceleration) integrates planet and planetesimal dynamics in the late stage of planet formation and stability analyses of planetary systems. It uses mixed variable integration when the motion is a perturbed Kepler orbit and combines this with a direct N-body Bulirsch-Stoer method during close encounters. It supports three simulation modes: 1.) integration of up to 2048 massive bodies; 2.) integration with up to a million test particles; and 3.) parallel integration of a large number of individual planetary systems.
GenPK generates the 3D matter power spectra for each particle species from a Gadget snapshot. Written in C++, it requires both FFTW3 and GadgetReader.
Relativistic radiative transfer problems require the calculation of photon trajectories in curved spacetime. Programmed in Fortran, Geokerr uses a novel technique for rapid and accurate calculation of null geodesics in the Kerr metric. The equations of motion from the Hamilton-Jacobi equation are reduced directly to Carlson's elliptic integrals, simplifying algebraic manipulations and allowing all coordinates to be computed semi-analytically for the first time.
George is a fast and flexible library, implemented in C++ with Python bindings, for Gaussian Process regression useful for accounting for correlated noise in astronomical datasets, including those for transiting exoplanet discovery and characterization and stellar population modeling.
The GetData Project is the reference implementation of the Dirfile Standards, a filesystem-based, column-oriented database format for time-ordered binary data. Dirfiles provide a fast, simple format for storing and reading data, suitable for both quicklook and analysis pipelines. GetData provides a C API and bindings exist for various other languages. GetData is distributed under the terms of the GNU Lesser General Public License.
GetDist analyzes Monte Carlo samples, including correlated samples from Markov Chain Monte Carlo (MCMC). It offers a point and click GUI for selecting chain files, viewing plots, marginalized constraints, and LaTeX tables, and includes a plotting library for making custom publication-ready 1D, 2D, 3D-scatter, triangle and other plots. Its convergence diagnostics include correlation length and diagonalized Gelman-Rubin statistics, and the optimized kernel density estimation provides an automated optimal bandwidth choice for 1D and 2D densities with boundary and bias correction. It is available as a standalong package and with CosmoMC (ascl:1106.025).
getimages performs background derivation and image flattening for high-resolution images obtained with space observatories. It is based on median filtering with sliding windows corresponding to a range of spatial scales from the observational beam size up to a maximum structure width X. The latter is a single free parameter of getimages that can be evaluated manually from the observed image. The median filtering algorithm provides a background image for structures of all widths below X. The same median filtering procedure applied to an image of standard deviations derived from a background-subtracted image results in a flattening image. Finally, a flattened image is computed by dividing the background-subtracted by the flattening image. Standard deviations in the flattened image are now uniform outside sources and filaments. Detecting structures in such radically simplified images results in much cleaner extractions that are more complete and reliable. getimages also reduces various observational and map-making artifacts and equalizes noise levels between independent tiles of mosaicked images. The code (a Bash script) uses FORTRAN utilities from getsources (ascl:1507.014), which must be installed.
getsf extracts sources and filaments in astronomical images by separating their structural components, and is designed to handle multi-wavelength sets of images and very complex filamentary backgrounds. The method spatially decomposes the original images and separates the structural components of sources and filaments from each other and from their backgrounds, flattening their resulting images. It spatially decomposes the flattened components, combines them over wavelengths, and detects the positions of sources and skeletons of filaments. Finally, getsf measures the detected sources and filaments and creates the output catalogs and images. This universal and fully automated method has a single user-definable free parameter, which reduces to a minimum dependence of its results on the human factor.
getsources is a powerful multi-scale, multi-wavelength source extraction algorithm. It analyzes fine spatial decompositions of original images across a wide range of scales and across all wavebands, cleans those single-scale images of noise and background, and constructs wavelength-independent single-scale detection images that preserve information in both spatial and wavelength dimensions. getsources offers several advantages over other existing methods of source extraction, including the filtering out of irrelevant spatial scales to improve detectability, especially in the crowded regions and for extended sources, the ability to combine data over all wavebands, and the full automation of the extraction process.
The N-body code gevolution complies with general relativity principles at every step; it calculates all six metric degrees of freedom in Poisson gauge. N-body particles are evolved by solving the geodesic equation written in terms of a canonical momentum to remain valid for relativistic particles. gevolution can be extended to include different kinds of dark energy or modified gravity models, going beyond the usually adopted quasi-static approximation. A weak field expansion is the central element of gevolution; this permits the code to treat settings in which no strong gravitational fields appear, including arbitrary scenarios with relativistic sources as long as gravitational fields are not very strong. The framework is well suited for cosmology, but may also be useful for astrophysical applications with moderate gravitational fields where a Newtonian treatment is insufficient.
GFARGO is a GPU version of FARGO (ascl:1102.017). It is written in C and C for CUDA and runs only on NVIDIA’s graphics cards. Though it corresponds to the standard, isothermal version of FARGO, not all functionalities of the CPU version have been translated to CUDA. The code is available in single and double precision versions, the latter compatible with FERMI architectures. GFARGO can run on a graphics card connected to the display, allowing the user to see in real time how the fields evolve.
GGADT uses anomalous diffraction theory (ADT) to compute the differential scattering cross section (or the total cross sections as a function of energy) for a specified grain of arbitrary geometry (natively supports spheres, ellipsoids, and clusters of spherical monomers). It is written in Fortran 95. ADT is valid when the grain is large compared to the wavelength of incident light. GGADT can calculate either the integrated cross sections (absorption, scattering, extinction) as a function of energy, or it can calculate the differential scattering cross section as a function of scattering angle.
Ggm contains useful utilities for Gaussian gradient filtering of astronomical FITS images. It applies the Gaussian gradient magnitude filter to an input fits image, using a particular scale, sigma, in pixels. ggm cosmetically hides point sources in fits images by filling point sources with random values from the surrounding pixel region. It also provides an interactive tool to combine FITS images filtered on different scales.
GGobi is an open source visualization program for exploring high-dimensional data. It provides highly dynamic and interactive graphics such as tours, as well as familiar graphics such as the scatterplot, barchart and parallel coordinates plots. Plots are interactive and linked with brushing and identification.
GIBIS is a pixel-level simulator of the Gaia mission. It is intended to simulate how the Gaia instruments will observe the sky, using realistic simulations of the astronomical sources and of the instrumental properties. It is a branch of the global Gaia Simulator under development within the Gaia DPAC CU2 Group (Data Simulations). Access is currently restricted to Gaia DPAC teams.
Observations of disk galaxies at z~2 have demonstrated that turbulence driven by gravitational instability can dominate the energetics of the disk. GIDGET is a 1D simulation code, which we have made publicly available, that economically evolves these galaxies from z~2 to z~0 on a single CPU in a matter of minutes, tracking column density, metallicity, and velocity dispersions of gaseous and multiple stellar components. We include an H$_2$ regulated star formation law and the effects of stellar heating by transient spiral structure. We use this code to demonstrate a possible explanation for the existence of a thin and thick disk stellar population and the age-velocity dispersion correlation of stars in the solar neighborhood: the high velocity dispersion of gas in disks at z~2 decreases along with the cosmological accretion rate, while at lower redshift, the dynamically colder gas forms the low velocity dispersion stars of the thin disk.
GILDAS is a collection of software oriented toward (sub-)millimeter radioastronomical applications (either single-dish or interferometer). It has been adopted as the IRAM standard data reduction package and is jointly maintained by IRAM & CNRS. GILDAS contains many facilities, most of which are oriented towards spectral line mapping and many kinds of 3-dimensional data. The code, written in Fortran-90 with a few parts in C/C++ (mainly keyboard interaction, plotting, widgets), is easily extensible.
GIM2D (Galaxy IMage 2D) is an IRAF/SPP package written to perform detailed bulge/disk decompositions of low signal-to-noise images of distant galaxies in a fully automated way. GIM2D takes an input image from HST or ground-based telescopes and outputs a galaxy-subtracted image as well as a catalog of structural parameters.
Ginga is a viewer for astronomical data FITS (Flexible Image Transport System) files; the viewer centers around a FITS display widget which supports zooming and panning, color and intensity mapping, a choice of several automatic cut levels algorithms and canvases for plotting scalable geometric forms. In addition to this widget, the FITS viewer provides a flexible plugin framework for extending the viewer with many different features. A fairly complete set of "standard" plugins are provided for expected features of a modern viewer: panning and zooming windows, star catalog access, cuts, star pick/fwhm, thumbnails, and others. This viewer was written by software engineers at Subaru Telescope, National Astronomical Observatory of Japan, and is in use at that facility.
GIPSY is an acronym of Groningen Image Processing SYstem. It is a highly interactive software system for the reduction and display of astronomical data. It supports multi-tasking using a versatile user interface, it has an advanced data structure, a powerful script language and good display facilities based on the X Window system.
GIPSY consists of a number of components which can be divided into a number of classes:
GiRaFFE leverages the Einstein Toolkit's (ascl:1102.014) highly-scalable infrastructure to create large-scale simulations of magnetized plasmas in strong, dynamical spacetimes on adaptive-mesh refinement (AMR) grids. It is based on IllinoisGRMHD (ascl:2004.003), a user-friendly, open-source, dynamical-spacetime GRMHD code, and is highly scalable, to tens of thousands of cores.
GIST (Galaxy IFU Spectroscopy Tool) provides a convenient all-in-one framework for the scientific analysis of fully reduced, (integral-field) spectroscopic data, conducting all the steps from the preparation of input data to the scientific analysis and to the production of publication-quality plots. In its basic set-up, the GIST pipeline extracts stellar kinematics, performs an emission-line analysis, and derives stellar population properties from full spectral fitting and via the measurement of absorption line-strength indices by exploiting pPXF (ascl:1210.002)and GandALF routines. The pipeline is not specific to any instrument or analysis technique, and includes a dedicated visualization routine with a sophisticated graphical user interface for fully interactive plotting of all measurements, spectra, fits, and residuals, as well as star formation histories and the weight distribution of the models.
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