Results 301-350 of 2195 (2158 ASCL, 37 submitted)
Traditionally, a simulation of a dense stellar system required choosing an initial model, running an integrator, and analyzing the output. Almost all of the effort went into writing a clever integrator that could handle binaries, triples and encounters between various multiple systems efficiently. Recently, the scope and complexity of these simulations has increased dramatically, for three reasons: 1) the sheer size of the data sets, measured in Terabytes, make traditional 'awking and grepping' of a single output file impractical; 2) the addition of stellar evolution data brings qualitatively new challenges to the data reduction; 3) increased realism of the simulations invites realistic forms of 'SOS': Simulations of Observations of Simulations, to be compared directly with observations. We are now witnessing a shift toward the construction of archives as well as tailored forms of visualization including the use of virtual reality simulators and planetarium domes, and a coupling of both with budding efforts in constructing virtual observatories. This review describes these new trends, presenting Starlab as the first example of a full software environment for realistic large-scale simulations of dense stellar systems.
Starfish is a set of tools used for spectroscopic inference. It robustly determines stellar parameters using high resolution spectral models and uses Markov Chain Monte Carlo (MCMC) to explore the full posterior probability distribution of the stellar parameters. Additional potential applications include other types of spectra, such as unresolved stellar clusters or supernovae spectra.
StarFISH is a suite of programs designed to determine the star formation history (SFH) of a stellar population, given multicolor stellar photometry and a library of theoretical isochrones. It constructs a library of synthetic color-magnitude diagrams from the isochrones, which includes the effects of extinction, photometric errors and completeness, and binarity. A minimization routine is then used to determine the linear combination of synthetic CMDs that best matches the observed photometry. The set of amplitudes modulating each synthetic CMD describes the star formation history of the observed stellar population.
StarFinder is an IDL code for the deep analysis of stellar fields, designed for Adaptive Optics well-sampled images with high and low Strehl ratio. The Point Spread Function is extracted directly from the frame, to take into account the actual structure of the instrumental response and the atmospheric effects. The code is written in IDL language and organized in the form of a self-contained widget-based application, provided with a series of tools for data visualization and analysis. A description of the method and some applications to Adaptive Optics data are presented.
StarCrash is a parallel fortran code based on Smoothed Particle Hydrodynamics (SPH) techniques to calculate the 3-d evolution of self-gravitating fluid systems. The code in particularly suited to the study of stellar interactions, such as mergers of binary star systems and stellar collisions. The StarCrash code comes with several important features, including:
StarburstPy is a python wrapper for Starburst99 (ascl:1104.003). The code contains methods for setting all inputs, running Starburst99, and reading output data into python dictionaries.
Starburst99 is a comprehensive set of model predictions for spectrophotometric and related properties of galaxies with active star formation. The models are presented in a homogeneous way for five metallicities between Z = 0.040 and 0.001 and three choices of the initial mass function. The age coverage is 10^6 to 10^9 yr. Spectral energy distributions are used to compute colors and other quantities.
STARBLADE (STar and Artefact Removal with a Bayesian Lightweight Algorithm from Diffuse Emission) separates superimposed point-like sources from a diffuse background by imposing physically motivated models as prior knowledge. The algorithm can also be used on noisy and convolved data, though performing a proper reconstruction including a deconvolution prior to the application of the algorithm is advised; the algorithm could also be used within a denoising imaging method. STARBLADE learns the correlation structure of the diffuse emission and takes it into account to determine the occurrence and strength of a superimposed point source.
Database management is an increasingly important part of astronomical data analysis. Astronomers need easy and convenient ways of storing, editing, filtering, and retrieving data about data. Commercial databases do not provide good solutions for many of the everyday and informal types of database access astronomers need. The Starbase database system with simple data file formatting rules and command line data operators has been created to answer this need. The system includes a complete set of relational and set operators, fast search/index and sorting operators, and many formatting and I/O operators. Special features are included to enhance the usefulness of the database when manipulating astronomical data. The software runs under UNIX, MSDOS and IRAF.
Stan facilitates statistical inference at the frontiers of applied statistics and provides both a modeling language for specifying complex statistical models and a library of statistical algorithms for computing inferences with those models. These components are exposed through interfaces in environments such as R, Python, and the command line.
Stagger is an astrophysical MHD code actively used to model star formation. It is equipped with a multi-frequency radiative transfer module and a comprehensive equation of state module that includes a large number of atomic and molecular species, to be able to compute realistic 3-D models of the near-surface layers of stars. The current version of the code allows a discretization that explicitly conserves mass, momentum, energy, and magnetic flux. The tensor formulation of the viscosity ensures that the viscous force is insensitive to the coordinate system orientation, thereby avoiding artificial grid-alignment.
STACKER stacks sources in interferometric data, i.e., averaging emission from different sources. The library allows stacking to be done directly on visibility data as well as in the image domain. The code is in format of a CASA (ascl:1107.013) task and implements uv- and image-stacking algorithms; it also provides several useful tasks for stacking related data processing. It allows introduction and stacking of random sources to estimate bias and noise, and also allows removal of a model of bright sources from the data.
The ssos pipeline detects and identifies known and unknown Solar System Objects (SSOs) in astronomical images. ssos requires at least 3 images with overlapping field-of-views in the sky taken within a reasonable amount of time (e.g., 2 hours, 1 night). SSOs are detected mainly by judging the apparent motion of all sources in the images. The pipeline serves as a wrapper for the SExtractor (ascl:1010.064) and SCAMP (ascl:1010.063) software suites and allows different source extraction strategies to be chosen. All sources in the images are subject to a highly configurable filter pipeline. ssos is a versatile, light-weight, and easy-to-use software for surveys or PI-observation campaigns lacking a dedicated SSO detection pipeline.
SSMM (Slotted Symbolic Markov Modeling) reduces time-domain stellar variable observations to classify stellar variables. The method can be applied to both folded and unfolded data, and does not require time-warping for waveform alignment. Written in Matlab, the performance of the supervised classification code is quantifiable and consistent, and the rate at which new data is processed is dependent only on the computational processing power available.
SSE is a rapid single-star evolution (SSE) code; these analytical formulae cover all phases of evolution from the zero-age main-sequence up to and including remnant phases. It is valid for masses in the range 0.1-100 Msun and metallicity can be varied. The SSE package contains a prescription for mass loss by stellar winds. It also follows the evolution of rotational angular momentum for the star.
SPTCLASS assigns semi-automatic spectral types to a sample of stars. The main code includes three spectral classification schemes: the first one is optimized to classify stars in the mass range of TTS (K5 or later, hereafter LATE-type scheme); the second one is optimized to classify stars in the mass range of IMTTS (F late to K early, hereafter Gtype scheme), and the third one is optimized to classify stars in the mass range of HAeBe (F5 or earlier, hereafter HAeBe scheme). SPTCLASS has an interactive module that allows the user to select the best result from the three schemes and analyze the input spectra.
The SPT lensing likelihood code, written in Fortran90, performs a Gaussian likelihood based upon the lensing potential power spectrum using a file from CAMB (ascl:1102.026) which contains the normalization required to get the power spectrum that the likelihood call is expecting.
The SPS software simulates the operation of the Spectral and Photometric Imaging Receiver on-board the ESA’s Herschel Space Observatory. It is coded using the Interactive Data Language (IDL), and produces simulated data at the level-0 stage (non-calibrated data in digitised units). The primary uses for the simulator are to:
The presence of human-made interference mimicking the behavior of celestial radio pulses is a major challenge when searching for radio pulses emitted on millisecond timescales by celestial radio sources such as pulsars and fast radio bursts due to the highly imbalanced samples. Single-pulse Searcher (SpS) reduces the presence of radio interference when processing standard output from radio single-pulse searches to produce diagnostic plots useful for selecting good candidates. The modular software allows modifications for specific search characteristics. LOTAAS Single-pulse Searcher (L-SpS) is an implementation of different features of the software (such as a machine-learning approach) developed for a particular study: the LOFAR Tied-Array All-Sky Survey (LOTAAS).
SPRITE (Sparse Recovery of InstrumenTal rEsponse) computes a well-resolved compact source image from several undersampled and noisy observations. The algorithm is based on sparse regularization; adding a sparse penalty in the recovery leads to far better accuracy in terms of ellipticity error, especially at low S/N.
SPOTROD is a model for planetary transits of stars with an arbitrary limb darkening law and a number of homogeneous, circular spots on their surface. It facilitates analysis of anomalies due to starspot eclipses, and is a free, open source implementation written in C with a Python API.
spops is a database of populations synthesis simulations of spinning black-hole binary systems, together with a python module to query it. Data are obtained with the startrack and precession [ascl:1611.004] numerical codes to consistently evolve binary stars from formation to gravitational-wave detection. spops allows quick exploration of the interplay between stellar physics and black-hole spin dynamics.
Splotch is a light and fast, publicly available, ray-tracer software tool which supports the effective visualization of cosmological simulations data. The algorithm it relies on is designed to deal with point-like data, optimizing the ray-tracing calculation by ordering the particles as a function of their 'depth', defined as a function of one of the coordinates or other associated parameters. Realistic three-dimensional impressions are reached through a composition of the final colour in each pixel properly calculating emission and absorption of individual volume elements.
SPLAT is a graphical tool for displaying, comparing, modifying and analyzing astronomical spectra stored in NDF, FITS and TEXT files as well as in NDX format. It can read in many spectra at the same time and then display these as line plots. Display windows can show one or several spectra at the same time and can be interactively zoomed and scrolled, centered on specific wavelengths, provide continuous coordinate readout, produce printable hardcopy and be configured in many ways. Analysis facilities include the fitting of a polynomial to selected parts of a spectrum, the fitting of Gaussian, Lorentzian and Voigt profiles to emission and absorption lines and the filtering of spectra using average, median and line-shape window functions as well as wavelet denoising. SPLAT also supports a full range of coordinate systems for spectra, which allows coordinates to be displayed and aligned in many different coordinate systems (wavelength, frequency, energy, velocity) and transformed between these and different standards of rest (topocentric, heliocentric, dynamic and kinematic local standards of rest, etc). SPLAT is distributed as part of the Starlink (ascl:1110.012) software collection.
SPLAT-VO is an extension of the SPLAT (Spectral Analysis Tool, ascl:1402.007) graphical tool for displaying, comparing, modifying and analyzing astronomical spectra; it includes facilities that allow it to work as part of the Virtual Observatory (VO). SPLAT-VO comes in two different forms, one for querying and downloading spectra from SSAP servers and one for interoperating with VO tools, such as TOPCAT (ascl:1101.010).
SPLASH (formerly SUPERSPHPLOT) visualizes output from (astrophysical) simulations using the Smoothed Particle Hydrodynamics (SPH) method in one, two and three dimensions. Written in Fortran 90, it uses the PGPLOT graphics subroutine library for plotting. It is based around a command-line menu structure but utilizes the interactive capabilities of PGPLOT to manipulate data interactively in the plotting window. SPLASH is fully interactive; visualizations can be changed rapidly at the touch of a button (e.g. zooming, rotating, shifting cross section positions etc). Data is read directly from the code dump format giving rapid access to results and the visualization is advanced forwards and backwards through timesteps by single keystrokes. SPLASH uses the SPH kernel to render plots of not only density but other physical quantities, giving a smooth representation of the data.
Spirality measures spiral arm pitch angles by fitting galaxy images to spiral templates of known pitch. Written in MATLAB, the code package also includes GenSpiral, which produces FITS images of synthetic spirals, and SpiralArmCount, which uses a one-dimensional Fast Fourier Transform to count the spiral arms of a galaxy after its pitch is determined.
SPIPS (Spectro-Photo-Interferometry of Pulsating Stars) combines radial velocimetry, interferometry, and photometry to estimate physical parameters of pulsating stars, including presence of infrared excess, color excess, Teff, and ratio distance/p-factor. The global model-based parallax-of-pulsation method is implemented in Python. Derived parameters have a high level of confidence; statistical precision is improved (compared to other methods) due to the large number of data taken into account, accuracy is improved by using consistent physical modeling and reliability of the derived parameters is strengthened by redundancy in the data.
SPIDERz (SuPport vector classification for IDEntifying Redshifts) applies powerful support vector machine (SVM) optimization and statistical learning techniques to custom data sets to obtain accurate photometric redshift (photo-z) estimations. It is written for the IDL environment and can be applied to traditional data sets consisting of photometric band magnitudes, or alternatively to data sets with additional galaxy parameters (such as shape information) to investigate potential correlations between the extra galaxy parameters and redshift.
SPIDERMAN calculates exoplanet phase curves and secondary eclipses with arbitrary surface brightness distributions in two dimensions. The code uses a geometrical algorithm to solve exactly the area of sections of the disc of the planet that are occulted by the star. Approximately 1000 models can be generated per second in typical use, which makes making Markov Chain Monte Carlo analyses practicable. The code is modular and allows comparison of the effect of multiple different brightness distributions for a dataset.
SpiceyPy is a Python wrapper for the NAIF C SPICE Toolkit (ascl:1903.015). It is compatible with Python 2 and 3, and was written using ctypes.
The SPICE (Spacecraft Planet Instrument C-matrix [“Camera matrix”] Events) toolkit offers a set of building blocks for constructing tools supporting multi-mission, international space exploration programs and research in planetary science, heliophysics, Earth science, and for observations from terrestrial observatories. It computes many kinds of observation geometry parameters, including the ephemerides, orientations, sizes, and shapes of planets, satellites, comets and asteroids. It can also compute the orientation of a spacecraft, its various moving structures, and an instrument's field-of-view location on a planet's surface or atmosphere. It can determine when a specified geometric event occurs, such as when an object is in shadow or is in transit across another object. The SPICE toolkit is available in FORTRAN 77, ANSI C, IDL, and MATLAB.
SPHYNX addresses subsonic hydrodynamical instabilities and strong shocks; it is Newtonian, grounded on the Euler-Lagrange formulation of the smoothed-particle hydrodynamics technique, and density based. SPHYNX uses an integral approach for estimating gradients, a flexible family of interpolators to suppress pairing instability, and incorporates volume elements to provides better partition of the unity.
SPHRAY, a Smoothed Particle Hydrodynamics (SPH) ray tracer, is designed to solve the 3D, time dependent, radiative transfer (RT) equations for arbitrary density fields. The SPH nature of SPHRAY makes the incorporation of separate hydrodynamics and gravity solvers very natural. SPHRAY relies on a Monte Carlo (MC) ray tracing scheme that does not interpolate the SPH particles onto a grid but instead integrates directly through the SPH kernels. Given initial conditions and a description of the sources of ionizing radiation, the code will calculate the non-equilibrium ionization state (HI, HII, HeI, HeII, HeIII, e) and temperature (internal energy/entropy) of each SPH particle. The sources of radiation can include point like objects, diffuse recombination radiation, and a background field from outside the computational volume. The MC ray tracing implementation allows for the quick introduction of new physics and is parallelization friendly. A quick Axis Aligned Bounding Box (AABB) test taken from computer graphics applications allows for the acceleration of the raytracing component. We present the algorithms used in SPHRAY and verify the code by performing all the test problems detailed in the recent Radiative Transfer Comparison Project of Iliev et. al. The Fortran 90 source code for SPHRAY and example SPH density fields are made available online.
SPHGR (Smoothed-Particle Hydrodynamics Galaxy Reduction) is a python based open-source framework for analyzing smoothed-particle hydrodynamic simulations. Its basic form can run a baryonic group finder to identify galaxies and a halo finder to identify dark matter halos; it can also assign said galaxies to their respective halos, calculate halo & galaxy global properties, and iterate through previous time steps to identify the most-massive progenitors of each halo and galaxy. Data about each individual halo and galaxy is collated and easy to access.
SPHGR supports a wide range of simulations types including N-body, full cosmological volumes, and zoom-in runs. Support for multiple SPH code outputs is provided by pyGadgetReader (ascl:1411.001), mainly Gadget (ascl:0003.001) and TIPSY (ascl:1111.015).
Spheroid determines the size distribution of polarizing interstellar dust grains based on electromagnetic scattering by spheroidal particles. It contains subroutines to treat the case of complex refractive indices, and also includes checks for some limiting cases.
The Spherical Library provides an efficient and accurate mathematical representation of shapes on the celestial sphere, such as sky coverage and footprints. Shapes of arbitrary complexity and size can be dynamically created from simple building blocks, whose exact area is also analytically computed. This methodology is also perfectly suited for censoring problematic parts of datasets, e.g., bad seeing, satellite trails or diffraction spikes of bright stars.
Spheral++ provides a steerable parallel environment for performing coupled hydrodynamical and gravitational numerical simulations. Hydrodynamics and gravity are modeled using particle-based methods (SPH and N-Body). It uses an Adaptive Smoothed Particle Hydrodynamics (ASPH) algorithm, provides a total energy conserving compatible hydro mode, and performs fluid and solid material modeling and damage and fracture modeling in solids.
We describe a fast tree algorithm for gravitational N-body simulation on SIMD parallel computers. The tree construction uses fast, parallel sorts. The sorted lists are recursively divided along their x, y and z coordinates. This data structure is a completely balanced tree (i.e., each particle is paired with exactly one other particle) and maintains good spatial locality. An implementation of this tree-building algorithm on a 16k processor Maspar MP-1 performs well and constitutes only a small fraction (approximately 15%) of the entire cycle of finding the accelerations. Each node in the tree is treated as a monopole. The tree search and the summation of accelerations also perform well. During the tree search, node data that is needed from another processor is simply fetched. Roughly 55% of the tree search time is spent in communications between processors. We apply the code to two problems of astrophysical interest. The first is a simulation of the close passage of two gravitationally, interacting, disk galaxies using 65,636 particles. We also simulate the formation of structure in an expanding, model universe using 1,048,576 particles. Our code attains speeds comparable to one head of a Cray Y-MP, so single instruction, multiple data (SIMD) type computers can be used for these simulations. The cost/performance ratio for SIMD machines like the Maspar MP-1 make them an extremely attractive alternative to either vector processors or large multiple instruction, multiple data (MIMD) type parallel computers. With further optimizations (e.g., more careful load balancing), speeds in excess of today's vector processing computers should be possible.
Spextool (Spectral EXtraction tool) is an IDL-based data reduction package for SpeX, a medium resolution near-infrared spectrograph on the NASA IRTF. It performs all of the steps necessary to produce spectra ready for analysis and publication including non-linearity corrections, flat fielding, wavelength calibration, telluric correction, flux calibration, and order merging.
SPEX is optimized for the analysis and interpretation of high-resolution cosmic X-ray spectra. The software is especially suited for fitting spectra obtained by current X-ray observatories like XMM-Newton, Chandra, and Suzaku. SPEX can fit multiple spectra with different model components simultaneously and handles highly complex models with many free parameters.
SPEGID (Single-Pulse Event Group IDentification) identifies astrophysical pulse candidates as trial single-pulse event groups (SPEGs) by first applying Density Based Spatial Clustering of Applications with Noise (DBSCAN) on trial single-pulse events and then merging the clusters that fall within the expected DM (Dispersion Measure) and time span of astrophysical pulses. SPEGID also calculates the peak score for each SPEG in the S/N versus DM space to identify the expected peak-like shape in the signal-to-noise (S/N) ratio versus DM curve of astrophysical pulses. Additionally, SPEGID groups SPEGs that appear at a consistent DM and therefore are likely emitted from the same source. After running SPEGID, periocity.py can be used to find (or verify) the underlying periodicity among a group of SPEGs (i.e., astrophysical pulse candidates).
SPECX is a general purpose line data reduction system. It can read and write FITS data cubes but has specialist support for the GSD format data from the James Clerk Maxwell Telescope. It includes commands to store and retrieve intermediate spectra in storage registers and perform the fitting and removal of polynomial, harmonic and Gaussian baselines.
SPECX can filter and edit spectra and list and display spectra on a graphics terminal. It is able to perform Fourier transform and power spectrum calculations, process up to eight spectra (quadrants) simultaneously with either the same or different center, and assemble a number of reduced individual spectra into a map file and contour or greyscale any plane or planes of the resulting cube.
Two versions of SPECX are distributed. Version 6.x is the VMS and Unix version and is distributed as part of the Starlink software collection. Version 7.x is a complete rewrite of SPECX distributed for Windows.
SpecViz interactively visualizes and analyzes 1D astronomical spectra. It reads data from FITS and ASCII tables and allows spectra to be easily plotted and examined. It supports instrument-specific data quality handling, flexible spectral units conversions, custom plotting attributes, plot annotations, tiled plots, among other features. SpecViz includes a measurement tool for spectral lines for performing and recording measurements and a model fitting capability for creating simple (e.g., single Gaussian) or multi-component models (e.g., multiple Gaussians for emission and absorption lines in addition to regions of flat continua). SpecViz is built on top of the Specutils (ascl:1902.012) Astropy-affiliated python library, providing a visual, interactive interface to the analysis capabilities in that library.
Specview is a tool for 1-D spectral visualization and analysis of astronomical spectrograms. Written in Java, it is capable of reading all the Hubble Space Telescope spectral data formats as well as data from several other instruments (such as IUE, FUSE, ISO, FORS and SDSS), preview spectra from MAST, and data from generic FITS and ASCII tables. It can read data from Virtual Observatory servers, and read and write spectrogram data in Virtual Observatory SED format. It can also read files in the SPC Galactic format used in the chemistry field. Once ingested, data can be plotted and examined with a large selection of custom settings. Specview supports instrument-specific data quality handling, flexible spectral units conversions, custom plotting attributes, plot annotations, tiled plots, hardcopy to JPEG files and PostScript file or printer, etc. Specview can be used to build wide-band SEDs, overplotting or combining data from the same astronomical source taken with different instruments and/or spectral bands. Data can be further processed with averaging, splicing, detrending, and Fourier filtering tools. Specview has a spectral model fitting capability that enables the user to work with multi-component models (including user-defined models) and fit models to data.
Specutils provides a basic interface for the loading, manipulation, and common forms of analysis of spectroscopic data. Its generic data containers and accompanying modules can be used to build a particular scientific workflow or higher-level analysis tool. It is an AstroPy (ascl:1304.002) affiliated package, and SpecViz (ascl:1902.011), which is built on top of Specutils, provides a visual, interactive interface to its analysis capabilities.
SPECTRUM ((C) Richard O. Gray, 1992-2008) is a stellar spectral synthesis program which runs on a number of platforms, including most flavors of UNIX and LINUX. It will also run under Windwos 9x/ME/NT/2000/XP using the Cygwin tools or the distributed Windows binaries. The code for SPECTRUM has been written in the "C" language. SPECTRUM computes the LTE synthetic spectrum given a stellar atmosphere model. SPECTRUM can use as input the fully blanketed stellar atmosphere models of Robert Kurucz including the new models of Castelli and Kurucz, but any other stellar atmosphere model which can be cast into the format of Kurucz's models can be used as well. SPECTRUM can be programmed with "command-line switches" to give a number of different outputs. In the default mode, SPECTRUM computes the stellar-disk-integrated normalized-intensity spectrum, but in addition, SPECTRUM will compute the absolute monochromatic flux from the stellar atmosphere or the specific intensity from any point on the stellar surface.
SPECTRE's chief purpose is the manipulation of single-order spectra, and it performs many of the tasks contained in such IRAF routines as "splot" and "rv". It is not meant to replace the much more general capabilities of IRAF, but does some functions in a manner that some might find useful. A brief list of SPECTRE tasks are: spectrum smoothing; equivalent width calculation; continuum rectification; noise spike excision; and spectrum comparison. SPECTRE was written to manipulate coude spectra, and thus is probably most useful for working on high dispersion spectra. Echelle spectra can be gathered from various observatories, reduced to singly-dimensioned spectra using IRAF, then written out as FITS files, thus becoming accessible to SPECTRE. Three different spectra may be manipulated and displayed simultaneously. SPECTRE, written in standard FORTRAN77, can be used only with the SM graphics package.
Spectral-cube provides an easy way to read, manipulate, analyze, and write data cubes with two positional dimensions and one spectral dimension, optionally with Stokes parameters. It is a versatile data container for building custom analysis routines. It provides a uniform interface to spectral cubes, robust to the wide range of conventions of axis order, spatial projections, and spectral units that exist in the wild, and allows easy extraction of cube sub-regions using physical coordinates. It has the ability to create, combine, and apply masks to datasets and is designed to work with datasets too large to load into memory, and provide basic summary statistic methods like moments and array aggregates.
Spectra calculates the power spectrum of a time series equally spaced or not based on the Spectral Correlation Coefficient (Ferraz-Mello 1981, Astron. Journal 86 (4), 619). It is very efficient for detection of low frequencies.
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