Results 1801-1850 of 1927 (1899 ASCL, 28 submitted)
This three-component package provides a Pythonic implementation of the Nested Sampling integration algorithm for Bayesian model comparison and parameter estimation. It offers multiple implementations for constrained drawing functions and a test suite to evaluate the correctness, accuracy and efficiency of various implementations. The three components are:
ULySS (University of Lyon Spectroscopic Analysis Software) is an open-source software package written in the GDL/IDL language to analyze astronomical data. ULySS fits a spectrum with a linear combination of non-linear components convolved with a line-of-sight velocity distribution (LOSVD) and multiplied by a polynomial continuum. ULySS is used to study stellar populations of galaxies and star clusters and atmospheric parameters of stars.
Astrochemistry database of chemical species.
UniDAM obtains a homogenized set of stellar parameters from spectrophotometric data of different surveys. Parallax and extinction data can be incorporated into the isochrone fitting method used in UniDAM to reduce distance and age estimate uncertainties for TGAS stars for distances up to 1 kpc and decrease distance Gaia end-of-mission parallax uncertainties by about a factor of 20 and age uncertainties by a factor of two for stars up to 10 kpc away from the Sun.
The equation of state (EOS) of dense matter is a crucial input for the neutron-star structure calculations. This Fortran code can obtain a "unified EOS" in the many-body calculations based on a single effective nuclear Hamiltonian, and is valid in all regions of the neutron star interior. For unified EOSs, the transitions between the outer crust and the inner crust and between the inner crust and the core are obtained as a result of many-body calculations.
UniPOPS, a suite of programs and utilities developed at the National Radio Astronomy Observatory (NRAO), reduced data from the observatory's single-dish telescopes: the Tucson 12-m, the Green Bank 140-ft, and archived data from the Green Bank 300-ft. The primary reduction programs, 'line' (for spectral-line reduction) and 'condar' (for continuum reduction), used the People-Oriented Parsing Service (POPS) as the command line interpreter. UniPOPS unified previous analysis packages and provided new capabilities; development of UniPOPS continued within the NRAO until 2004 when the 12-m was turned over to the Arizona Radio Observatory (ARO). The submitted code is version 3.5 from 2004, the last supported by the NRAO.
Univiewer is a visualisation program for HEALPix maps. It is written in C++ and uses OpenGL and the wxWidgets library for cross-platform portability. Using it you can:
In the 3D view, a HEALPix map is projected onto a ECP pixelation to create a texture which is wrapped around the sphere. In calculating the power spectrum, the spherical harmonic transforms are computed using the same ECP pixelation. This inevitably leads to some discrepancies at small scales due to repixelation effects, but they are reasonably small.
The unwise_psf Python module renders point spread function (PSF) models appropriate for use in modeling of unWISE coadd images. unwise_psf translates highly detailed single-exposure WISE PSF models in detector coordinates to the corresponding pixelized PSF models in coadd space, accounting for subtleties including the WISE scan direction and its considerable variation near the ecliptic poles. Applications of the unwise_psf module include performing forced photometry on unWISE coadds, constructing WISE-selected source catalogs based on unWISE coadds and masking unWISE coadd regions contaminated by bright stars.
UPMASK, written in R, performs membership assignment in stellar clusters. It uses photometry and spatial positions, but can take into account other types of data. UPMASK takes into account arbitrary error models; the code is unsupervised, data-driven, physical-model-free and relies on as few assumptions as possible. The approach followed for membership assessment is based on an iterative process, principal component analysis, a clustering algorithm and a kernel density estimation.
UPSILoN (AUtomated Classification of Periodic Variable Stars using MachIne LearNing) classifies periodic variable stars such as Delta Scuti stars, RR Lyraes, Cepheids, Type II Cepheids, eclipsing binaries, and long-period variables (i.e. superclasses), and their subclasses (e.g. RR Lyrae ab, c, d, and e types) using well-sampled light curves from any astronomical time-series surveys in optical bands regardless of their survey-specific characteristics such as color, magnitude, and sampling rate. UPSILoN consists of two parts, one which extracts variability features from a light curve, and another which classifies a light curve, and returns extracted features, a predicted class, and a class probability. In principle, UPSILoN can classify any light curves having arbitrary number of data points, but using light curves with more than ~80 data points provides the best classification quality.
URCHIN is a Smoothed Particle Hydrodynamics (SPH) reverse ray tracer (i.e. from particles to sources). It calculates the amount of shielding from a uniform background that each particle experiences. Preservation of the adaptive density field resolution present in many gas dynamics codes and uniform sampling of gas resolution elements with rays are two of the benefits of URCHIN; it also offers preservation of Galilean invariance, high spectral resolution, and preservation of the standard uniform UV background in optically thin gas.
The util_2comp software utilities generate predictions of far-infrared Galactic dust emission and reddening based on a two-component dust emission model fit to Planck HFI, DIRBE and IRAS data from 100 GHz to 3000 GHz. These predictions and the associated dust temperature map have angular resolution of 6.1 arcminutes and are available over the entire sky. Implementations in IDL and Python are included.
The Universal Transit Modeller (UTM) is a light-curve simulator for all kinds of transiting or eclipsing configurations between arbitrary numbers of several types of objects, which may be stars, planets, planetary moons, and planetary rings. A separate fitting program, UFIT (Universal Fitter) is part of the UTM distribution and may be used to derive best fits to light-curves for any set of continuously variable parameters. UTM/UFIT is written in IDL code and its source is released in the public domain under the GNU General Public License.
Uvmcmcfit fits parametric models to interferometric data. It is ideally suited to extract the maximum amount of information from marginally resolved observations with interferometers like the Atacama Large Millimeter Array (ALMA), Submillimeter Array (SMA), and Plateau de Bure Interferometer (PdBI). uvmcmcfit uses emcee (ascl:1303.002) to do Markov Chain Monte Carlo (MCMC) and can measure the goodness of fit from visibilities rather than deconvolved images, an advantage when there is strong gravitational lensing and in other situations. uvmcmcfit includes a pure-Python adaptation of Miriad’s (ascl:1106.007) uvmodel task to generate simulated visibilities given observed visibilities and a model image and a simple ray-tracing routine that allows it to account for both strongly lensed systems (where multiple images of the lensed galaxy are detected) and weakly lensed systems (where only a single image of the lensed galaxy is detected).
UVMULTIFIT, written in Python, is a versatile library for fitting models directly to visibility data. These models can depend on frequency and fitting parameters in an arbitrary algebraic way. The results from the fit to the visibilities of sources with sizes smaller than the diffraction limit of the interferometer are superior to the output obtained from a mere analysis of the deconvolved images. Though UVMULTIFIT is based on the CASA package, it can be easily adapted to other analysis packages that have a Python API.
The two Swift UVOT grisms provide uv (170.0-500.0 nm) and visible (285.0-660.0 nm) spectra with a resolution of R~100 and 75. To reduce the grism data, UVOTPY extracts a spectrum given source sky position, and outputs a flux calibrated spectrum. UVOTPY is a replacement for the UVOTIMGRISM FTOOL (ascl:9912.002) in the HEADAS Swift package. Its extraction uses a curved aperture for the uv spectra, accounts the coincidence losses in the detector, provides more accurate anchor positions for the wavelength scale, and is valid for the whole detector.
The Versatile Advection Code (VAC) is a freely available general hydrodynamic and magnetohydrodynamic simulation software that works in 1, 2 or 3 dimensions on Cartesian and logically Cartesian grids. VAC runs on any Unix/Linux system with a Fortran 90 (or 77) compiler and Perl interpreter. VAC can run on parallel machines using either the Message Passing Interface (MPI) library or a High Performance Fortran (HPF) compiler.
VADER is a flexible, general code that simulates the time evolution of thin axisymmetric accretion disks in time-steady potentials. VADER handles arbitrary viscosities, equations of state, boundary conditions, and source and sink terms for both mass and energy.
VaeX (Visualization and eXploration) interactively visualizes and explores big tabular datasets. It can calculate statistics such as mean, sum, count, and standard deviation on an N-dimensional grid up to a billion (109) objects/rows per second. Visualization is done using histograms, density plots, and 3d volume rendering, allowing interactive exploration of big data. VaeX uses memory mapping, zero memory copy policy and lazy computations for best performance, and integrates well with the Jupyter/IPython notebook/lab ecosystem.
Validation provides codes to compare several observations to simulated data with stellar mass and star formation rate, simulated data stellar mass function with observed stellar mass function from PRIMUS or SDSS-GALEX in several redshift bins from 0.01-1.0, and simulated data B band luminosity function with observed stellar mass function, and to create plots for various attributes, including stellar mass functions, and stellar mass to halo mass. These codes can model predictions (in some cases alongside observational data) to test other mock catalogs.
VAPHOT is an aperture photometry package for precise time−series photometry of uncrowded fields, geared towards the extraction of target lightcurves of eclipsing or transiting systems. Its photometric main routine works within the IRAF (ascl:9911.002) environment and is built upon the standard aperture photometry task 'phot' from IRAF, using optimized aperture sizes. The associated analysis program 'VANALIZ' works in the IDL environment. It performs differential photometry with graphical and numerical output. VANALIZ produces plots indicative of photometric stability and permits the interactive evaluation and weighting of comparison stars. Also possible is the automatic or manual suppression of data-points and the output of statistical analyses. Several methods for the calculation of the reference brightness are offered. Specific routines for the analysis of transit 'on'-'off' photometry, comparing the target brightness inside against outside a transit are also available.
VAPID (Voigt Absorption Profile [Interstellar] Dabbler) models interstellar absorption lines. It predicts profiles and optimizes model parameters by least-squares fitting to observed spectra. VAPID allows cloud parameters to be optimized with respect to several different data set simultaneously; those data sets may include observations of different transitions of a given species, and may have different S/N ratios and resolutions.
VAPOR is the Visualization and Analysis Platform for Ocean, Atmosphere, and Solar Researchers. VAPOR provides an interactive 3D visualization environment that runs on most UNIX and Windows systems equipped with modern 3D graphics cards. VAPOR provides:
The Variable Star Analysis Library (JVarStar) is an open source Java Maven package that represents an accumulation of methods and techniques used in the analysis of variable star data for the purposes of pattern classification. Machine learning techniques, fundamental mathematical methods, and digital signal processing functions are included in this all-in-one package that can be externally referenced (i.e., from Python), or can be used for further Java development. In addition to the developed functionality, this library has dependencies on several open source packages such as: maven, Apache Math Commons, JUnit, JSOFA, etc. The unification of these dependencies along with the developed functionality, provides a developer with an easily accessible library from which to construct stable variable star analysis and classification code.
The VARTOOLS program is a command line utility that provides tools for analyzing time series astronomical data. It implements a number of routines for calculating variability/periodicity statistics of light curves, as well as tools for modifying and filtering light curves.
VaST (Variability Search Toolkit) finds variable objects on a series of astronomical images in FITS format. The software performs object detection and aperture photometry using SExtractor (ascl:1010.064) on each image, cross-matches lists of detected stars, performs magnitude calibration with respect to the first (reference) image and constructs a lightcurve for each object. The sigma-magnitude, Stetson's L variability index, Robust Median Statistic (RoMS) and other plots may be used to visually identify variable star candidates. The two distinguishing features of VaST are its ability to perform accurate aperture photometry of images obtained with non-linear detectors and to handle complex image distortions. VaST can be used in cases of unstable PSF (e.g., bad guiding or with digitized wide-field photographic images), and has been successfully applied to images obtained with telescopes ranging from 0.08 to 2.5m in diameter equipped with a variety of detectors including CCD, CMOS, MIC and photographic plates.
VBBinaryLensing forward models gravitational microlensing events using the advanced contour integration method; it supports single and binary lenses. The lens map is inverted on a collection of points on the source boundary to obtain a corresponding collection of points on the boundaries of the images from which the area of the images can be recovered by use of Green’s theorem. The code takes advantage of a number of techniques to make contour integration much more efficient, including using a parabolic correction to increase the accuracy of the summation, introducing an error estimate on each arc of the boundary to enable defining an optimal sampling, and allowing the inclusion of limb darkening. The code is written as a C++ library and wrapped as a Python package, and can be called from either C++ or Python.
Velbin convolves the radial velocity offsets due to binary orbital motions with a Gaussian to model an observed velocity distribution. This can be used to measure the mean velocity and velocity dispersion from an observed radial velocity distribution, corrected for binary orbital motions. Velbin fits single- or multi-epoch data with any arbitrary binary orbital parameter distribution (as long as it can be sampled properly), however it always assumes that the intrinsic velocity distribution (i.e. corrected for binary orbital motions) is a Gaussian. Velbin samples (and edits) a binary orbital parameter distribution, fits an observed radial velocity distribution, and creates a mock radial velocity distribution that can be used to provide the fitted radial velocities in the single_epoch or multi_epoch methods.
High-quality velocity maps of galaxies frequently exhibit signatures of non-circular streaming motions. velfit yields results that are more easily interpreted than the commonly used procedure. It can estimate the magnitudes of forced non-circular motions over a broad range of bar strengths from a strongly barred galaxy, through cases of mild bar-like distortions to placing bounds on the shapes of halos in galaxies having extended rotation curves.
This code is no longer maintained and has been superseded by DiskFit (ascl:1209.011).
venice reads a mask file (DS9 or fits type) and a catalogue of objects (ascii or fits type) to create a pixelized mask, find objects inside/outside a mask, or generate a random catalogue of objects inside/outside a mask. The program reads the mask file and checks if a point, giving its coordinates, is inside or outside the mask, i.e. inside or outside at least one polygon of the mask.
Verne calculates the Earth-stopping effect for super-heavy Dark Matter (DM). The code allows you to calculate the speed distribution (and DM signal rate) at an arbitrary detector location on the Earth. The calculation takes into account the full anisotropic DM velocity distribution and the full velocity dependence of the DM-nucleus cross section. Results can be obtained for any DM mass and cross section, though the results are most reliable for very heavy DM particles.
Validation of Exoplanet Signals using a Probabilistic Algorithm (VESPA) calculates false positive probabilities and statistically validates transiting exoplanets. Written in Python, it uses isochrones [ascl:1503.010] and the package simpledist.
VH-1 is a multidimensional ideal compressible hydrodynamics code written in FORTRAN for use on any computing platform, from desktop workstations to supercomputers. It uses a Lagrangian remap version of the Piecewise Parabolic Method developed by Paul Woodward and Phil Colella in their 1984 paper. VH-1 comes in a variety of versions, from a simple one-dimensional serial variant to a multi-dimensional version scalable to thousands of processors.
VHD is a numerical study of viscous fluid accretion onto a black hole. The flow is axisymmetric and uses a pseudo-Newtonian potential to model relativistic effects near the event horizon. VHD is based on ZEUS-2D (Stone & Norman 1992) with the addition of an explicit scheme for the viscosity.
The Victoria–Regina stellar models are comprised of seventy-two grids of stellar evolutionary tracks accompanied by complementary zero-age horizontal branches and are presented in “equivalent evolutionary phase” (.eep) files. This Fortran 77 software interpolates isochrones, isochrone population functions, luminosity functions, and color functions of stellar evolutionary tracks.
The Void IDentification and Examination toolkit (VIDE) identifies voids using a modified version of the parameter-free void finder ZOBOV (ascl:1304.005); a Voronoi tessellation of the tracer particles is used to estimate the density field followed by a watershed algorithm to group Voronoi cells into zones and subsequently voids. Output is a summary of void properties in plain ASCII; a Python API is provided for analysis tasks, including loading and manipulating void catalogs and particle members, filtering, plotting, computing clustering statistics, stacking, comparing catalogs, and fitting density profiles.
Viewpoints is an interactive tool for exploratory visual analysis of large high-dimensional (multivariate) data. It uses linked scatterplots to find relations in a few seconds that can take much longer with other plotting tools. Its features include linked scatter plots with brushing, dynamic histograms, normalization, and outlier detection/removal.
VIM (Virtual Observatory Integration and Mining) is a data retrieval and exploration application that assumes an astronomer has a list of 'sources' (positions in the sky), and wants to explore archival catalogs, images, and spectra of the sources, in order to identify, select, and mine the list. VIM does this either through web forms, building a custom 'data matrix,' or locally through downloadable Python code. Any VO-registered catalog service can be used by VIM, as well as co-registered image cutouts from VO-image services, and spectra from VO-spectrum services. The user could, for example, show together: proper motions from GSC2, name and spectral type from NED, magnitudes and colors from 2MASS, and cutouts and spectra from SDSS. VIM can compute columns across surveys and sort on these (eg. 2MASS J magnitude minus SDSS g). For larger sets of sources, VIM utilizes the asynchronous Nesssi services from NVO, that can run thousands of cone and image services overnight.
VINE is a particle based astrophysical simulation code. It uses a tree structure to efficiently solve the gravitational N-body problem and Smoothed Particle Hydrodynamics (SPH) to simulate gas dynamical effects. The code has been successfully used for a number of studies on galaxy interactions, galactic dynamics, star formation and planet formation and given the implemented physics, other applications are possible as well.
VIP (Vortex Image Processing pipeline) provides pre- and post-processing algorithms for high-contrast direct imaging of exoplanets. Written in Python, VIP provides a very flexible framework for data exploration and image processing and supports high-contrast imaging observational techniques, including angular, reference-star and multi-spectral differential imaging. Several post-processing algorithms for PSF subtraction based on principal component analysis are available as well as the LLSG (Local Low-rank plus Sparse plus Gaussian-noise decomposition) algorithm for angular differential imaging. VIP also implements the negative fake companion technique coupled with MCMC sampling for rigorous estimation of the flux and position of potential companions.
VirGO is the next generation Visual Browser for the ESO Science Archive Facility developed by the Virtual Observatory (VO) Systems Department. It is a plug-in for the popular open source software Stellarium adding capabilities for browsing professional astronomical data. VirGO gives astronomers the possibility to easily discover and select data from millions of observations in a new visual and intuitive way. Its main feature is to perform real-time access and graphical display of a large number of observations by showing instrumental footprints and image previews, and to allow their selection and filtering for subsequent download from the ESO SAF web interface. It also allows the loading of external FITS files or VOTables, the superimposition of Digitized Sky Survey (DSS) background images, and the visualization of the sky in a `real life' mode as seen from the main ESO sites. All data interfaces are based on Virtual Observatory standards which allow access to images and spectra from external data centers, and interaction with the ESO SAF web interface or any other VO applications supporting the PLASTIC messaging system.
ViSBARD interactively visualizes and analyzes space physics data. It provides an interactive integrated 3-D and 2-D environment to determine correlations between measurements across many spacecraft. It supports a variety of spacecraft data products and MHD models and is easily extensible to others. ViSBARD provides a way of visualizing multiple vector and scalar quantities as measured by many spacecraft at once. The data are displayed three-dimesionally along the orbits which may be displayed either as connected lines or as points. The data display allows the rapid determination of vector configurations, correlations between many measurements at multiple points, and global relationships. With the addition of magnetohydrodynamic (MHD) model data, this environment can also be used to validate simulation results with observed data, use simulated data to provide a global context for sparse observed data, and apply feature detection techniques to the simulated data.
VISIBLE applies approximated matched filters to interferometric data, allowing line detection directly in visibility space. The filter can be created from a FITS image or RADMC3D output image, and the weak line data can be a CASA MS or uvfits file. The filter response spectrum can be output either to a .npy file or returned back to the user for scripting.
VisIt is a free interactive parallel visualization and graphical analysis tool for viewing scientific data on Unix and PC platforms. Users can quickly generate visualizations from their data, animate them through time, manipulate them, and save the resulting images for presentations. VisIt contains a rich set of visualization features so that you can view your data in a variety of ways. It can be used to visualize scalar and vector fields defined on two- and three-dimensional (2D and 3D) structured and unstructured meshes. VisIt was designed to handle very large data set sizes in the terascale range and yet can also handle small data sets in the kilobyte range. See the table below for more details about the tool’s features.
VisIt was developed by the Department of Energy (DOE) Advanced Simulation and Computing Initiative (ASCI) to visualize and analyze the results of terascale simulations. It was developed as a framework for adding custom capabilities and rapidly deploying new visualization technologies. Although the primary driving force behind the development of VisIt was for visualizing terascale data, it is also well suited for visualizing data from typical simulations on desktop systems.
VisIVO is an integrated suite of tools and services specifically designed for the Virtual Observatory. This suite constitutes a software framework for effective visual discovery in currently available (and next-generation) very large-scale astrophysical datasets. VisIVO consists of VisiVO Desktop - a stand alone application for interactive visualization on standard PCs, VisIVO Server - a grid-enabled platform for high performance visualization and VisIVO Web - a custom designed web portal supporting services based on the VisIVO Server functionality. The main characteristic of VisIVO is support for high-performance, multidimensional visualization of very large-scale astrophysical datasets. Users can obtain meaningful visualizations rapidly while preserving full and intuitive control of the relevant visualization parameters. This paper focuses on newly developed integrated tools in VisIVO Server allowing intuitive visual discovery with 3D views being created from data tables. VisIVO Server can be installed easily on any web server with a database repository. We discuss briefly aspects of our implementation of VisiVO Server on a computational grid and also outline the functionality of the services offered by VisIVO Web. Finally we conclude with a summary of our work and pointers to future developments.
Vissage (VISualisation Software for Astronomical Gigantic data cubEs) is a FITS browser primarily targeting FITS data cubes obtained from ALMA. Vissage offers basic functionality for viewing three-dimensional data cubes, integrated intensity map, flipbook, channel map, and P-V diagram. It has several color sets and color scales available, offers panning and zooming, and can connect with the ALMA WebQL system and the JVO Subaru Image Cutout Service.
Vizic is a Python visualization library that builds the connection between images and catalogs through an interactive map of the sky region. The software visualizes catalog data over a custom background canvas using the shape, size and orientation of each object in the catalog and displays interactive and customizable objects in the map. Property values such as redshift and magnitude can be used to filter or apply colormaps, and objects can be selected for further analysis through standard Python functions from inside a Jupyter notebook.
Vizic allows custom overlays to be appended dynamically on top of the sky map; included are Voronoi, Delaunay, Minimum Spanning Tree and HEALPix layers, which are helpful for visualizing large-scale structure. Overlays can be generated, added or removed dynamically with one line of code. Catalog data is kept in a non-relational database. The Jupyter Notebook allows the user to create scripts to analyze and plot the data selected/displayed in the interactive map, making Vizic a powerful and flexible interactive analysis tool. Vizic be used for data inspection, clustering analysis, galaxy alignment studies, outlier identification or simply large-scale visualizations.
VOBOZ (VOronoi BOund Zones) is an algorithm to find haloes in an N-body dark matter simulation which has little dependence on free parameters.
ZOBOV (ZOnes Bordering On Voidness) is an algorithm that finds density depressions in a set of points without any free parameters or assumptions about shape. It uses the Voronoi tessellation to estimate densities to find both voids and subvoids. It also measures probabilities that each void or subvoid arises from Poisson fluctuations.
voevent-parse, written in Python, parses, manipulates, and generates VOEvent XML packets; it is built atop lxml.objectify. Details of transients detected by many projects, including Fermi, Swift, and the Catalina Sky Survey, are currently made available as VOEvents, which is also the standard alert format by future facilities such as LSST and SKA. However, working with XML and adhering to the sometimes lengthy VOEvent schema can be a tricky process. voevent-parse provides convenience routines for common tasks, while allowing the user to utilise the full power of the lxml library when required. An earlier version of voevent-parse was part of the pysovo (ascl:1411.002) library.
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