Results 251-300 of 2256 (2217 ASCL, 39 submitted)
SWOT (Super W Of Theta) computes two-point statistics for very large data sets, based on “divide and conquer” algorithms, mainly, but not limited to data storage in binary trees, approximation at large scale, parellelization (open MPI), and bootstrap and jackknife resampling methods “on the fly”. It currently supports projected and 3D galaxy auto and cross correlations, galaxy-galaxy lensing, and weighted histograms.
SWOC (Spectral Wavelength Optimization Code) determines the wavelength ranges that provide the optimal amount of information to achieve the required science goals for a spectroscopic study. It computes a figure-of-merit for different spectral configurations using a user-defined list of spectral features, and, utilizing a set of flux-calibrated spectra, determines the spectral regions showing the largest differences among the spectra.
SwiftVis is a tool originally developed as part of a rewrite of Swift to be used for analysis and plotting of simulations performed with Swift and Swifter. The extensibility built into the design has allowed us to make SwiftVis a general purpose analysis and plotting package customized to be usable by the planetary science community at large. SwiftVis is written in Java and has been tested on Windows, Linux, and Mac platforms. Its graphical interface allows users to do complex analysis and plotting without having to write custom code.
SWIFT runs cosmological simulations on peta-scale machines for solving gravity and SPH. It uses the Fast Multipole Method (FMM) to calculate gravitational forces between nearby particles, combining these with long-range forces provided by a mesh that captures both the periodic nature of the calculation and the expansion of the simulated universe. SWIFT currently uses a single fixed but time-variable softening length for all the particles. Many useful external potentials are also available, such as galaxy haloes or stratified boxes that are used in idealised problems. SWIFT implements a standard LCDM cosmology background expansion and solves the equations in a comoving frame; equations of state of dark-energy evolve with scale-factor. The structure of the code allows implementation for modified-gravity solvers or self-interacting dark matter schemes to be implemented. Many hydrodynamics schemes are implemented in SWIFT and the software allows users to add their own.
SWIFT follows the long-term dynamical evolution of a swarm of test particles in the solar system. The code efficiently and accurately handles close approaches between test particles and planets while retaining the powerful features of recently developed mixed variable symplectic integrators. Four integration techniques are included: Wisdom-Holman Mapping; Regularized Mixed Variable Symplectic (RMVS) method; fourth order T+U Symplectic (TU4) method; and Bulirsch-Stoer method. The package is designed so that the calls to each of these look identical so that it is trivial to replace one with another. Complex data manipulations and results can be analyzed with the graphics packace SwiftVis.
SWarp resamples and co-adds together FITS images using any arbitrary astrometric projection defined in the WCS standard. It operates on pre-reduced images and their weight-maps. Based on the astrometric and photometric calibrations derived at an earlier phase of the pipeline, SWarp re-maps ("warps") the pixels to a perfect projection system, and co-adds them in an optimum way, according to their relative weights. SWarp's astrometric engine is based on a customized version of Calabretta's WCSLib 2.6 and supports all of the projections defined in the 2000 version of the WCS proposal.
Swarm-NG is a C++ library for the efficient direct integration of many n-body systems using highly-parallel Graphics Processing Units (GPU). Swarm-NG focuses on many few-body systems, e.g., thousands of systems with 3...15 bodies each, as is typical for the study of planetary systems; the code parallelizes the simulation, including both the numerical integration of the equations of motion and the evaluation of forces using NVIDIA's "Compute Unified Device Architecture" (CUDA) on the GPU. Swarm-NG includes optimized implementations of 4th order time-symmetrized Hermite integration and mixed variable symplectic integration as well as several sample codes for other algorithms to illustrate how non-CUDA-savvy users may themselves introduce customized integrators into the Swarm-NG framework. Applications of Swarm-NG include studying the late stages of planet formation, testing the stability of planetary systems and evaluating the goodness-of-fit between many planetary system models and observations of extrasolar planet host stars (e.g., radial velocity, astrometry, transit timing). While Swarm-NG focuses on the parallel integration of many planetary systems,the underlying integrators could be applied to a wide variety of problems that require repeatedly integrating a set of ordinary differential equations many times using different initial conditions and/or parameter values.
surrkick quickly and reliably extract recoils imparted to generic, precessing, black hole binaries. It uses a numerical-relativity surrogate model to obtain the gravitational waveform given a set of binary parameters, and from this waveform directly integrates the gravitational-wave linear momentum flux. This entirely bypasses the need of fitting formulae which are typically used to model black-hole recoils in astrophysical contexts.
The Surprise is a measure for consistency between posterior distributions and operates in parameter space. It can be used to analyze either the compatibility of separately analyzed posteriors from two datasets, or the posteriors from a Bayesian update. The Surprise Calculator estimates relative entropy and Surprise between two samples, assuming they are Gaussian. The software requires the R package CompQuadForm to estimate the significance of the Surprise, and rpy2 to interface R with Python.
surfinBH predicts the final mass, spin and recoil velocity of the remnant of a binary black hole merger. Trained directly against numerical relativity simulations, these models are extremely accurate, reproducing the results of the simulations at the same level of accuracy as the simulations themselves. Fits such as these play a crucial role in waveform modeling and tests of general relativity with gravitational waves, performed by LIGO.
Superplot calculates and plots statistical quantities relevant to parameter inference from a "chain" of samples drawn from a parameter space produced by codes such as MultiNest (ascl:1109.006), BAYES-X (ascl:1505.027), and PolyChord (ascl:1502.011). It offers a graphical interface for browsing a chain of many variables quickly and can produce numerous kinds of publication quality plots, including one- and two-dimensional profile likelihood, three-dimensional scatter plots, and confidence intervals and credible regions. Superplot can also save plots in PDF format, create a summary text file, and export a plot as a pickled object for importing and manipulating in a Python interpreter.
Supernovae classifies supernovae using their light curves directly as inputs to a deep recurrent neural network, which learns information from the sequence of observations. Observational time and filter fluxes are used as inputs; since the inputs are agnostic, additional data such as host galaxy information can also be included.
Flux-averaging justifies the use of the distance-redshift relation for a smooth universe in the analysis of type Ia supernova (SN Ia) data. Flux-averaging of SN Ia data is required to yield cosmological parameter constraints that are free of the bias induced by weak gravitational lensing. SN Ia data are converted into flux. For a given cosmological model, the distance dependence of the data is removed, then the data are binned in redshift, and placed at the average redshift in each redshift bin. The likelihood of the given cosmological model is then computed using "flux statistics''. These Fortran codes compute the likelihood of an arbitrary cosmological model [with given H(z)/H_0] using flux-averaged Type Ia supernova data.
SuperFreq numerically estimates the fundamental frequencies and orbital actions of pre-computed orbital time series. It is an implementation of a version of the Numerical Analysis of Fundamental Frequencies close to that by Monica Valluri, which itself is an implementation of an algorithm first used by Jacques Laskar.
SUPERBOX is a particle-mesh code that uses moving sub-grids to track and resolve high-density peaks in the particle distribution and a nearest grid point force-calculation scheme based on the second derivatives of the potential. The code implements a fast low-storage FFT-algorithm and allows a highly resolved treatment of interactions in clusters of galaxies, such as high-velocity encounters between elliptical galaxies and the tidal disruption of dwarf galaxies, as sub-grids follow the trajectories of individual galaxies. SUPERBOX is efficient in that the computational overhead is kept as slim as possible and is also memory efficient since it uses only one set of grids to treat galaxies in succession.
SuperBoL calculates the bolometric lightcurves of Type II supernovae using observed photometry; it includes three different methods for calculating the bolometric luminosity: quasi-bolometric, direct, and bolometric correction. SuperBoL propagates uncertainties in the input data through the calculations made by the code, allowing for error bars to be included in plots of the lightcurve.
SuperBayeS is a package for fast and efficient sampling of supersymmetric theories. It uses Bayesian techniques to explore multidimensional SUSY parameter spaces and to compare SUSY predictions with observable quantities, including sparticle masses, collider observables, dark matter abundance, direct detection cross sections, indirect detection quantities etc. Scanning can be performed using Markov Chain Monte Carlo (MCMC) technology or even more efficiently by employing a new scanning technique called MultiNest (ascl:1109.006). which implements the nested sampling algorithm. Using MultiNest, a full 8-dimensional scan of the CMSSM takes about 12 hours on 10 2.4GHz CPUs. There is also an option for old-style fixed-grid scanning. A discussion forum for SuperBayeS is available.
The package combines SoftSusy, DarkSusy, FeynHiggs, Bdecay, MultiNest and MicrOMEGAs. Some of the routines and the plotting tools are based on CosmoMC.
SuperBayeS comes with SuperEGO, a MATLAB graphical user interface tool for interactive plotting of the results. SuperEGO has been developed by Rachid Lemrani and is based on CosmoloGUI by Sarah Bridle.
This IDL code creates a thick magneto-static structure with parameters of a typical sunspot in a solar like photosphere - chromosphere. The variable parameters are field strength on the axis, radius, and Wilson depression (displacement of the atmosphere on the axis with respect to the field-free atmosphere). Output are magnetic field vector, pressure and density distributions with radius and height. The structure has azimuthal symmetry. The codes are relatively self explanatory and the download packages contain README files.
Sunrise is a Monte Carlo radiation transfer code for calculating absorption and scattering of light to study the effects of dust in hydrodynamic simulations of interacting galaxies. It uses an adaptive mesh refinement grid to describe arbitrary geometries of emitting and absorbing/scattering media, with spatial dynamical range exceeding 104; it can efficiently generate images of the emerging radiation at arbitrary points in space and spectral energy distributions of simulated galaxies run with the Gadget, Gasoline, Arepo, Enzo or ART codes. In addition to the monochromatic radiative transfer typically used by Monte Carlo codes, Sunrise can propagate a range of wavelengths simultaneously. This "polychromatic" algorithm gives significant improvements in efficiency and accuracy when spectral features are calculated.
SunPy is a community-developed free and open-source software package for solar physics and is an alternative to the SolarSoft data analysis environment. SunPy provides data structures for representing the most common solar data types (images, lightcurves, and spectra) and integration with the Virtual Solar Observatory (VSO) and the Heliophysics Event Knowledgebase (HEK) for data acquisition.
Stuff is a program that simulates “perfect” astronomical catalogues. It generate object lists in ASCII which can read by the SkyMaker program to produce realistic astronomical fields. Stuff is part of the EFIGI development project.
An extension to synphot (ascl:1811.001), stsynphot implements synthetic photometry package for HST and JWST support. The software constructs spectra from various grids of model atmosphere spectra, parameterized spectrum models, and atlases of stellar spectrophotometry. It also simulates observations specific to HST and JWST, computes photometric calibration parameters for any supported instrument mode, and plots instrument-specific sensitivity curves and calibration target spectra.
The Space Telescope Science Data Analysis System (STSDAS) is a software package for reducing and analyzing astronomical data. It is layered on top of IRAF and provides general-purpose tools for astronomical data analysis as well as routines specifically designed for HST data. In particular, STSDAS contains all the programs used for the calibration and reduction of HST data in the STScI post-observation processing pipelines.
StringFast implements a method for efficient computation of the C_l spectra induced by a network of strings, which is fast enough to be used in Markov Chain Monte Carlo analyses of future data. This code allows the user to calculate TT, EE, and BB power spectra (scalar [for TT and EE], vector, and tensor modes) for "wiggly" cosmic strings. StringFast uses the output of the public code CMBACT (ascl:1106.023). The properties of the strings are described by four parameters: Gμ—dimensionless string tension; v—rms transverse velocity (as fraction of c); α—"wiggliness"; ξ—comoving correlation length of the string network. It is written as a Fortran 90 module.
streamgap-pepper computes the effect of subhalo fly-bys on cold tidal streams based on the action-angle representation of streams. A line-of-parallel-angle approach is used to calculate the perturbed distribution function of a given stream segment by undoing the effect of all impacts. This approach allows one to compute the perturbed stream density and track in any coordinate system in minutes for realizations of the subhalo distribution down to 10^5 Msun, accounting for the stream's internal dispersion and overlapping impacts. This code uses galpy (ascl:1411.008) and the streampepperdf.py galpy extension, which implements the fast calculation of the perturbed stream structure.
STools contains a variety of simple tools for spectroscopy, such as reading an IRAF-formatted (multispec) echelle spectrum in FITS, measuring the wavelength of the center of a line, Gaussian convolution, deriving synthetic photometry from an input spectrum, and extracting and interpolating a MARCS model atmosphere (standard composition).
STOKES was designed to perform three-dimensional radiative transfer simulations for astronomical applications. The code also considers the polarization properties of the radiation. The program is based on the Monte-Carlo method and treats optical and ultraviolet polarization induced by scattering off free electrons or dust grains. Emission and scattering regions can be arranged in various geometries within the model space, the computed continuum and line spectra can be evaluated at different inclinations and azimuthal viewing angles.
Stingray is a spectral-timing software package for astrophysical X-ray (and more) data. The package merges existing efforts for a (spectral-)timing package in Python and is composed of a library of time series methods (including power spectra, cross spectra, covariance spectra, and lags); scripts to load FITS data files from different missions; a simulator of light curves and event lists that includes different kinds of variability and more complicated phenomena based on the impulse response of given physical events (e.g. reverberation); and a GUI to ease the learning curve for new users.
The STIL Tool Set is a set of command-line tools based on STIL, the Starlink Tables Infrastructure Library. It deals with the processing of tabular data; the package has been designed for, but is not restricted to, astronomical tables such as object catalogues. Some of the tools are generic and can work with multiple formats (including FITS, VOTable, CSV, SQL and ASCII), and others are specific to the VOTable format. In some ways, STILTS forms the command-line counterpart of the GUI table analysis tool TOPCAT. The package is robust, fully documented, and designed for efficiency, especially with very large datasets.
Facilities offered include:
STIFF converts scientific FITS images to the more popular TIFF format for illustration purposes. Most FITS readers and converters do not do a proper job at converting FITS image data to 8 bits. 8-bit images stored in JPEG, PNG or TIFF files have the intensities implicitly stored in a non-linear way. Most current FITS image viewers and converters provide the user an incorrect translation of the FITS image content by simply rescaling linearly input pixel values. A first consequence is that the people working on astronomical images usually have to apply narrow intensity cuts or square-root or logarithmic intensity transformations to actually see something on their deep-sky images. A less obvious consequence is that colors obtained by combining images processed this way are not consistent across such a large range of surface brightnesses. Though with other software the user is generally afforded a choice of nonlinear transformations to apply in order to make the faint stuff stand out more clearly in the images, with the limited selection of choices provides, colors will not be accurately rendered, and some manual tweaking will be necessary. The purpose of STIFF is to produce beautiful pictures in an automatic and consistent way.
STiC is a MPI-parallel non-LTE inversion code for observed full-Stokes observations. The code processes lines from multiple atoms in non-LTE, including partial redistribution effects of scattered photons in angle and frequency of scattered photons (PRD), and can be used with model atmospheres that have a complex depth stratification without introducing artifacts.
stginga customizes Ginga to aid data analysis for the data supported by STScI (e.g., HST or JWST). For instance, it provides plugins and configuration files that understand HST and JWST data products.
STF is a general structure finder designed to find halos, subhaloes, and tidal debris in N-body simulations. The current version is designed to read in "particle data" (that is SPH N-body data), but a simple modification of the I/O can have it read grid data from Grid based codes.
This code has been updated and renamed to VELOCIraptor-STF (ascl:1911.020).
StePS (Stereographically Projected Cosmological Simulations) compactifies the infinite spatial extent of the Universe into a finite sphere with isotropic boundary conditions to simulate the evolution of the large-scale structure. This eliminates the need for periodic boundary conditions, which are a numerical convenience unsupported by observation and which modifies the law of force on large scales in an unrealistic fashion. StePS uses stereographic projection for space compactification and naive O(N2) force calculation; this arrives at a correlation function of the same quality more quickly than standard (tree or P3M) algorithms with similar spatial and mass resolution. The N2 force calculation is easy to adapt to modern graphics cards, hence StePS can function as a high-speed prediction tool for modern large-scale surveys.
stepped_luneburg investigates the scattered light properties of a Luneburg lens approximated as a series of concentric shells with discrete refractive indices. The optical Luneburg lens has promising applications for low-cost, continuous all-sky monitoring to obtain transit light curves of bright, nearby stars. This code implements a stack-based algorithm that tracks all reflected and refracted rays generated at each optical interface of the lens as described by Snell's law. The Luneburg lens model parameters, such as number of lens layers, the power-law that describes the refractive indices, the number of incident rays, and the initial direction of the incident wavefront can be altered to optimize lens performance. The stepped_luneburg module can be imported within the Python environment or used with scripting, and it is accompanied by two other modules, enc_int and int_map, that help the user to determine the resolving power of the lens and the strength of scattered light haloes for the purpose of quality assessment.
StePar computes the stellar atmospheric parameters Teff, log g, [Fe/H], and ξ of FGK-type stars using the Equivalent Width (EW) method. The code implements a grid of MARCS model atmospheres and uses the MOOG radiative transfer code (ascl:1202.009) and TAME (ascl:1503.003). StePar uses a Downhill Simplex minimization algorithm, running it twice for any given star, to compute the stellar atmospheric parameters.
stellarWakes uses stellar kinematic data to search for dark matter (DM) subhalos through their gravitational perturbations to the stellar phase-space distribution.
Cosmic ray electrons scatter on the photon fields around stars, including the sun, to create gamma rays by the inverse Compton effect. Stellarics computes the spectrum and angular distribution of this emission. The software also includes general-purpose routines for inverse Compton scattering on a given electron spectrum, for example for interstellar or astrophysical source modelling.
stellaR accesses and manipulates publicly available stellar evolutionary tracks and isochrones from the Pisa low-mass database. It retrieves and plots the required calculations from CDS, constructs by interpolation tracks or isochrones of compositions different to the ones available in the database, constructs isochrones for age not included in the database, and extracts relevant evolutionary points from tracks or isochrones.
STELLA is a one-dimensional multi-group radiation hydrodynamics code. STELLA incorporates implicit hydrodynamics coupled to a multi-group non-equilibrium radiative transfer for modeling SN II-L light curves. The non-equilibrium description of radiation is crucial for this problem since the presupernova envelope may be of low mass and very dilute. STELLA implicitly treats time dependent equations of the angular moments of intensity averaged over a frequency bin. Local thermodynamic equilibrium is assumed to determine the ionization levels of materials.
STECKMAP stands for STEllar Content and Kinematics via Maximum A Posteriori likelihood. It is a tool for interpreting galaxy spectra in terms of their stellar populations through the derivation of their star formation history, age-metallicity relation, kinematics and extinction. The observed spectrum is projected onto a temporal sequence of models of single stellar populations, so as to determine a linear combination of these models that best fits the observed spectrum. The weights of the various components of this linear combination indicate the stellar content of the population. This procedure is regularized using various penalizing functions. The principles of the method are detailed in Ocvirk et al. 2006.
statpl estimates the parameter of power-law distributed data and calculates goodness-of-fit tests for them. Many objects studied in astronomy follow a power-law distribution function (DF), for example the masses of stars or star clusters. Such data is often analyzed by generating a histogram and fitting a straight line to it. The parameters obtained in this way can be severely biased, and the properties of the underlying DF, such as its shape or a possible upper limit, are difficult to extract. statpl is an (effectively) bias-free estimator for the exponent and the upper limit.
STATCONT determines the continuum emission level in line-rich spectral data by inspecting the intensity distribution of a given spectrum by using different statistical approaches. The sigma-clipping algorithm provides the most accurate continuum level determination, together with information on the uncertainty in its determination; this uncertainty is used to correct the final continuum emission level. In general, STATCONT obtains accuracies of < 10 % in the continuum determination, and < 5 % in most cases. The main products of the software are the continuum emission level, together with its uncertainty, and data cubes containing only spectral line emission, i.e. continuum-subtracted data cubes. STATCONT also includes the option to estimate the spectral index or variation of the continuum emission with frequency.
Smoothed Particle Hydrodynamics (SPH) is a Lagrangian particle method that approximates a continuous fluid as discrete nodes, each carrying various parameters such as mass, position, velocity, pressure, and temperature. In an SPH simulation the resolution scales with the particle density; StarSmasher is able to handle both equal-mass and equal number-density particle models. StarSmasher solves for hydro forces by calculating the pressure for each particle as a function of the particle's properties - density, internal energy, and internal properties (e.g. temperature and mean molecular weight). The code implements variational equations of motion and libraries to calculate the gravitational forces between particles using direct summation on NVIDIA graphics cards. Using a direct summation instead of a tree-based algorithm for gravity increases the accuracy of the gravity calculations at the cost of speed. The code uses a cubic spline for the smoothing kernel and an artificial viscosity prescription coupled with a Balsara Switch to prevent unphysical interparticle penetration. The code also implements an artificial relaxation force to the equations of motion to add a drag term to the calculated accelerations during relaxation integrations. Initially called StarCrash, StarSmasher was developed originally by Rasio.
The Matlab starsense_algorithms package evaluates the performance of various star sensors through the implementation of centroiding, geometric voting and QUEST algorithms. The physical parameters of a star sensor are parametrized and by changing these parameters, performance estimators such as sky coverage, memory requirement, and timing requirements can be estimated for the selected star sensor.
We have developed a detailed stellar evolution code capable of following the simultaneous evolution of both stars in a binary system, together with their orbital properties. To demonstrate the capabilities of the code we investigate potential progenitors for the Type IIb supernova 1993J, which is believed to have been an interacting binary system prior to its primary exploding. We use our detailed binary stellar evolution code to model this system to determine the possible range of primary and secondary masses that could have produced the observed characteristics of this system, with particular reference to the secondary. Using the luminosities and temperatures for both stars (as determined by Maund et al. 2004) and the remaining mass of the hydrogen envelope of the primary at the time of explosion, we find that if mass transfer is 100 per cent efficient the observations can be reproduced by a system consisting of a 15 solar mass primary and a 14 solar mass secondary in an orbit with an initial period of 2100 days. With a mass transfer efficiency of 50 per cent, a more massive system consisting of a 17 solar mass primary and a 16 solar mass secondary in an initial orbit of 2360 days is needed. We also investigate some of the uncertainties in the evolution, including the effects of tidal interaction, convective overshooting and thermohaline mixing.
STARRY computes light curves for various applications in astronomy: transits and secondary eclipses of exoplanets, light curves of eclipsing binaries, rotational phase curves of exoplanets, light curves of planet-planet and planet-moon occultations, and more. By modeling celestial body surface maps as sums of spherical harmonics, STARRY does all this analytically and is therefore fast, stable, and differentiable. Coded in C++ but wrapped in Python, STARRY is easy to install and use.
StarPy derives the quenching star formation history (SFH) of a single galaxy through the Bayesian Markov Chain Monte Carlo method code emcee (ascl:1303.002). The sample function implements the emcee EnsembleSampler function for the galaxy colors input. Burn-in is run and calculated for the length specified before the sampler is reset and then run for the length of steps specified. StarPy provides the ability to use the look-up tables provided or creating your own.
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