Results 151-200 of 2074 (2041 ASCL, 33 submitted)
CausticFrog models the reaction of a system of orbiting particles to instantaneous mass loss. It applies to any spherically symmetric potential, and follows the radial evolution of shells of mass. CausticFrog tracks the inner and outer edge of each shell, whose radius evolves as a test particle. The amount of mass in each shell is fixed but multiple shells can overlap leading to higher densities.
FortesFit efficiently explores and discriminates between various spectral energy distributions (SED) models of astronomical sources. The Python package adds Bayesian inference to a framework that is designed for the easy incorporation and relative assessment of SED models, various fitting engines, and a powerful treatment of priors, especially those that may arise from non-traditional wave-bands such as the X-ray or radio emission, or from spectroscopic measurements. It has been designed with particular emphasis for its scalability to large datasets and surveys.
The CLEAR pipeline and library performs various tasks for the CANDELS Ly-alpha Emission at Reionization (CLEAR) experiment of deep Hubble grism observations of high-z galaxies. It interlaces images, models contamination of overlapping grism spectra, extracts source spectra, stacks the extracted source spectra, and estimates fits for sources redshifts and emission lines.
Deproject extends Sherpa (ascl:1107.005) to facilitate deprojection of two-dimensional annular X-ray spectra to recover the three-dimensional source properties. For typical thermal models, this includes the radial temperature and density profiles. This basic method is used for X-ray cluster analysis and is the basis for the XSPEC (ascl:9910.005) model project. The deproject module is written in Python and is straightforward to use and understand. The basic physical assumption of deproject is that the extended source emissivity is constant and optically thin within spherical shells whose radii correspond to the annuli used to extract the specta. Given this assumption, one constructs a model for each annular spectrum that is a linear volume-weighted combination of shell models.
repack re-packs and compresses line-transition data for radiative-transfer calculations. It identifies the strong lines that dominate the spectrum from the large-majority of weaker lines, returning a binary line-by-line (LBL) file with the strong lines info (wavenumber, Elow, gf, and isotope ID), and an ASCII file with the combined contribution of the weaker lines compressed into a continuum extinction coefficient (in cm-1 amagat-1) as function of wavenumber and temperature.
AutoBayes automatically generates customized algorithms from compact, declarative specifications in the data analysis domain, taking a statistical model as input and creating documented and optimized C/C++ code. The synthesis process uses Bayesian networks to enable problem decompositions and guide the algorithm derivation. Program schemas encapsulate advanced algorithms and data structures, and a symbolic-algebraic system finds closed-form solutions for problems and emerging subproblems. AutoBayes has been used to analyze planetary nebulae images taken by the Hubble Space Telescope, and can be applied to other scientific data analysis tasks.
CDAWeb (Coordinated Data Analysis Workshop Web) enables viewing essentially any data produced in Common Data Format/CDF with the ISTP/IACG Guidelines and supports interactive plotting of variables from multiple instruments on multiple investigations simultaneously on arbitrary, user-defined time-scales. It also supports data retrieval in both CDF or ASCII format. NASA's GSFC Space Physics Data Facility maintains a publicly available database that includes approximately 600 data variables from Geotail, Wind, Interball, Polar, SOHO, ancilliary spacecraft and ground-based investigations. CDAWeb includes high resolution digital data products that support event correlative science. The system combines the client-server user interface technology of the Web with a powerful set of customized routines based in the COTS Interactive Data Language (IDL) package to leverage the data format standards.
SMILI uses sparse sampling techniques and other regularization methods for interferometric imaging. The python-interfaced library is mainly designed for very long baseline interferometry, and has been under the active development primarily for the Event Horizon Telescope (EHT).
ehtim (eht-imaging) simulates and manipulates VLBI data and produces images with regularized maximum likelihood methods. The package contains several primary classes for loading, simulating, and manipulating VLBI data. The main classes are the Image, Array, Obsdata, Imager, and Caltable classes, which provide tools for loading images and data, producing simulated data from realistic u-v tracks, calibrating, inspecting, and plotting data, and producing images from data sets in various polarizations using various data terms and regularizers.
SimCADO is a python package which allows the user to simulate observations with any NIR/Vis imaging system. The package was originally designed to simulate images for the European extremely large telescope (ELT) and MICADO, however with the proper input it is capable of simulating observations from many different Telescope + Instrument configurations.
The documentation can be found here: https://simcado.readthedocs.io/en/latest/
CGS (Collisionless Galactic Simulator) uses Fourier techniques to solve the Possion equation ∇2Φ = 4πGρ, relating the mean potential Φ of a system to the mass density ρ. The angular dependence of the force is treated exactly in terms of the single-particle Legendre polynomials, which preserves accuracy and avoids systematic errors. The density is assigned to a radial grid by means of a cloud-in-cell scheme with a linear kernel, i.e., a particle contributes to the density of the two closest cells with a weight depending linearly on the distance from the center of the cell considered. The same kernel is then used to assign the force from the grid to the particle. The time step is chosen adaptively in such a way that particles are not allowed to cross more than one radial cell during one step. CGS is based on van Albada's code (1982) and is distributed in the NEMO (ascl:1010.051) Stellar Dynamics Toolbox.
GALAXY evolves (almost) isolated, collisionless stellar systems, both disk-like and ellipsoidal. In addition to the N-body code galaxy, which offers eleven different methods to compute the gravitational accelerations, the package also includes sophisticated set-up and analysis software. While not as versatile as tree codes, for certain restricted applications the particle-mesh methods in GALAXY are 50 to 200 times faster than a widely-used tree code. After reading in data providing the initial positions, velocities, and (optionally) masses of the particles, GALAXY compute the gravitational accelerations acting on each particle and integrates forward the velocities and positions of the particles for a short time step, repeating these two steps as desired. Intermediate results can be saved, as can the final moment in a state from which the integration could be resumed. Particles can have individual masses and their motion can be integrated using a range of time steps for greater efficiency; message-passing-interface (MPI) calls are available to enable GALAXY's use on parallel machines with high efficiency.
The ROSAT X-Ray Background Tool (sxrbg) calculates the average X-ray background count rate and statistical uncertainty in each of the six standard bands of the ROSAT All-Sky Survey (RASS) diffuse background maps (R1, R2, R4, R5, R6, R7) for a specified astronomical position and a search region consisting of either a circle with a specified radius or an annulus with specified inner and outer radii centered on the position. The values returned by the tool are in units of 10^-6 counts/second/arcminute^2. sxrbg can also create a count-rate-based spectrum file which can be used with XSpec (ascl:9910.005) to calculate fluxes and offers support for counts statistics (cstat), an alternative method for generating a background spectrum. HEASoft (ascl:1408.004) is a prerequisite for building. The code is in the public domain.
HelioPy provides a set of tools to download and read in data, and carry out other common data processing tasks for heliospheric and planetary physics. It handles a wide variety of solar and satellite data and builds upon the SpiceyPy package (ascl:1903.016) to provide an accessible interface for performing orbital calculations. It has also implemented a framework to perform transformations between some common coordinate systems.
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.
PLATON (PLanetary Atmospheric Transmission for Observer Noobs) calculates transmission spectra for exoplanets and retrieves atmospheric characteristics based on observed spectra; it is based on ExoTransmit (ascl:1611.005). PLATON supports the most common atmospheric parameters, such as temperature, metallicity, C/O ratio, cloud-top pressure, and scattering slope. It also has less commonly included features, such as a Mie scattering cloud model and unocculted starspot corrections.
Available in R and Python, the simple analytic scheme NFWdist performs highly efficient and exact sampling of the Navarro, Frenk & White (NFW) profile as a true probability distribution function, with the only variable being the concentration.
DAVE implements a pipeline to find and vet planets planets using data from NASA's K2 mission. The pipeline contains several modules tailored to particular aspects of the vetting procedures, using photocenter analysis to rule out background eclipsing binaries and flux time-series analysis to rule out odd–even differences, secondary eclipses, low-S/N events, variability other than a transit, and size of the transiting object.
AsPy computes the determinants of aspherical fluctuations on the spherical collapse background. Written in Python, this procedure includes analytic factorization and cancellation of the so-called `IR-divergences'—spurious enhanced contributions that appear in the dipole sector and are associated with large bulk flows.
GalIMF (Galaxy-wide Initial Mass Function) computes the galaxy-wide initial stellar mass function by integrating over a whole galaxy, parameterized by star formation rate and metallicity. The generated stellar mass distribution depends on the galaxy-wide star formation rate (SFR, which is related to the total mass of a galalxy) and the galaxy-wide metallicity. The code can generate a galaxy-wide IMF (IGIMF) and can also generate all the stellar masses within a galaxy with optimal sampling (OSGIMF). To compute the IGIMF or the OSGIMF, the GalIMF module contains all local IMF properties (e.g. the dependence of the stellar IMF on the metallicity, on the density of the star-cluster forming molecular cloud cores), and this software module can, therefore, be also used to obtain only the stellar IMF with various prescriptions, or to investigate other features of the stellar population such as what is the most massive star that can be formed in a star cluster.
PRF (Probabilistic Random Forest) is a machine learning algorithm for noisy datasets. The PRF is a modification of the long-established Random Forest (RF) algorithm, and takes into account uncertainties in the measurements (i.e., features) as well as in the assigned classes (i.e., labels). To do so, the Probabilistic Random Forest (PRF) algorithm treats the features and labels as probability distribution functions, rather than as deterministic quantities.
NIFTy (Numerical Information Field Theory) facilitates the construction of Bayesian field reconstruction algorithms for fields being defined over multidimensional domains. A NIFTy algorithm can be developed for 1D field inference and then be used in 2D or 3D, on the sphere, or on product spaces thereof. NIFTy5 is a complete redesign of the previous framework (ascl:1302.013), and requires only the specification of a probabilistic generative model for all involved fields and the data in order to be able to recover the former from the latter. This is achieved via Metric Gaussian Variational Inference, which also provides posterior samples for all unknown quantities jointly.
ICSF (Intensity Conserving Spectral Fitting) "corrects" (x,y) data in which the ordinate represents the average of a quantity over a finite interval in the abscissa. A typical example is spectral data, where the average intensity over a wavelength bin (the measured quantity) is assigned to the center of the bin. If the profile is curved, the average will be different from the discrete value at the bin center location. ICSF, written in IDL and available separately and as part of SolarSoft (ascl:1208.013), corrects the intensity using an iterative procedure and cubic spline. The corrected intensity equals the "true" intensity at bin center, rather than the average over the bin. Unlike other methods that are restricted to a single fitting function, typically a spline, ICSF can be used with any function, such as a cubic spline or a Gaussian, with slight changes to the code.
The R-package SimSpin measures the kinematics of a galaxy simulation as if it had been observed using an IFU. The functions included in the package can produce a kinematic data cube and measure the "observables" from this data cube, specifically the observable spin parameter λr. This package, once installed, is fully documented and tested.
Galmag computes galactic magnetic fields based on mean field dynamo theory. Written in Python, Galmag allows quick exploration of solutions to the mean field dynamo equation based on galaxy parameters specified by the user, such as the scale height profile and the galaxy rotation curves. The magnetic fields are solenoidal by construction and can be helical.
brutifus aids in post-processing datacubes from integral field spectrographs. The set of Python routines in the package handle generic tasks, such as the registration of a datacube WCS solution with the Gaia catalogue, the correction of Galactic reddening, or the subtraction of the nebular/stellar continuum on a spaxel-per-spaxel basis, with as little user interactions as possible. brutifus is modular, in that the order in which the post-processing routines are run is entirely customizable.
allesfitter provides flexible and robust inference of stars and exoplanets given photometric and radial velocity (RV) data. The software offers a rich selection of orbital and transit models, accommodating multiple exoplanets, multi-star systems, star spots, stellar flares, and various noise models. It features both parameter estimation and model selection. A graphical user interface is used to specify input parameters, and to easily run a nested sampling or Markov Chain Monte Carlo (MCMC) fit, producing publication-ready tables, LaTex code, and plots. allesfitter provides an inference framework that unites the versatile packages ellc (ascl:1603.016), aflare (flare model; Davenport et al. 2014), dynesty (ascl:1809.013), emcee (ascl:1303.002) and celerite (ascl:1709.008).
SIXTE simulates X-Ray telescope observation; the software performs instrument performance analyses and produces simulated event files for mission and analysis studies. SIXTE strives to find a compromise between exactness of the simulation and speed. Using calibration files such as the PSF, RMF and ARF makes efficient simulations possible at comparably high speed, even though they include nonlinear effects such as pileup. Setups for some current and future missions, such as XMM-Newton and Athena, are included in the package; others can be added by the user with relatively little effort through specifying the main instrument characteristics in a flexible, human-readable XML-based format. Properties of X-ray sources to be simulated are described in a detector-independent format, i.e., the same input can be used for simulating observations with all available instruments, and the same input can also be used for simulations with the SIMX simulator. The input files are easily generated from standard data such as XSPEC (ascl:9910.005) spectral models or FITS images with tools provided with the SIXTE distribution. The input data scale well from single point sources up to very complicated setups.
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.
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.
dyPolyChord implements dynamic nested sampling using the efficient PolyChord (ascl:1502.011) sampler to provide state-of-the-art nested sampling performance. Any likelihoods and priors which work with PolyChord can be used (Python, C++ or Fortran), and the output files produced are in the PolyChord format.
ExPRES (Exoplanetary and Planetary Radio Emission Simulator) reproduces the occurrence of CMI-generated radio emissions from planetary magnetospheres, exoplanets or star-planet interacting systems in time-frequency plane, with special attention given to computation of the radio emission beaming at and near its source. Physical information drawn from such radio observations may include the location and dynamics of the radio sources, the type of current system leading to electron acceleration and their energy and, for exoplanetary systems, the magnetic field strength, the orbital period of the emitting body and the rotation period, tilt and offset of the planetary magnetic field. Most of these parameters can be remotely measured only via radio observations. ExPRES code provides the proper framework of analysis and interpretation for past (Cassini, Voyager, Galileo), current (Juno, ground-based radio telescopes) and future (BepiColombo, Juice) observations of planetary radio emissions, as well as for future detection of radio emissions from exoplanetary systems.
Radynversion infers solar atmospheric properties during a solar flare. The code is based on an Invertible Neural Network (INN) that is trained to learn an approximate bijective mapping between the atmospheric properties of electron density, temperature, and bulk velocity (all as a function of altitude), and the observed Hα and Ca II λ8542 line profiles. As information is lost in the forward process of radiation transfer, this information is injected back into the model during the inverse process by means of a latent space; the training allows this latent space to be filled using an n-dimensional unit Gaussian distribution, where n is the dimensionality of the latent space. The code is based on a model trained by simulations made by RADYN, a 1D non-equilibrium radiation hydrodynamic model with good optically thick radiation treatment that does not consider magnetic effects.
BELLAMY is a cross-matching algorithm designed primarily for radio images, that aims to match all sources in the supplied target catalogue to sources in a reference catalogue by calculating the probability of a match. BELLAMY utilises not only the position of a source on the sky, but also the flux data to calculate this probability, determining the most probable match in the reference catalog to the target source. Additionally, BELLAMY attempts to undo any spatial distortion that may be affecting the target catalogue, by creating a model of the offsets of matched sources which is then applied to unmatched sources. This combines to produce an iterative cross-matching algorithm that provides the user with an obvious measure of how confident they should be with the results of a cross-match.
PINT (PINT Is Not Tempo3) analyzes high-precision pulsar timing data, enabling interactive data analysis and providing an extensible and flexible development platform for timing applications. PINT utilizes well-debugged public Python packages and modern software development practices (e.g., the NumPy and Astropy libraries, version control and development with git and GitHub, and various types of testing) for increased development efficiency and enhanced stability. PINT has been developed and implemented completely independently from traditional pulsar timing software such as TEMPO (ascl:1509.002) and Tempo2 (ascl:1210.015) and is a robust tool for cross-checking timing analyses and simulating data.
The RPFITS data file format records synthesis visibility data obtained from the Australia Telescope Compact Array (ATCA) at Narrabri, NSW. It is also used for single-dish spectral line data obtained from Parkes and Mopra, including Parkes multibeam data. RPFITS superficially resembles random group FITS, but differs in important respects, making it incompatible with standard FITS software such as FITSIO (ascl:1010.001) and FTOOLS (ascl:9912.002) and, in particular, it precludes the use of fv (ascl:1205.005). The RPFITS Fortran library contains routines for reading and writing RPFITS files. A header file, RPFITS.h, is provided to facilitate usage by C and C++ applications. Also included is rpfhdr, a utility for viewing RPFITS headers (it also works for standard FITS), and rpfex for extracting selected scans from an RPFITS file.
LiveData is a multibeam single-dish data reduction system for bandpass calibration and gridding. It is used for processing Parkes multibeam and Mopra data.
GraviDy performs N-body 3D visualizations; it is a GPU, direct-summation N-body integrator based on the Hermite scheme and includes relativistic corrections for sources of gravitational radiation. The software is modular, allowing users to readily introduce new physics, and exploits available computational resources. The software can be used in parallel on multiple CPUs and GPUs, with a considerable speed-up benefit. The single-GPU version is between one and two orders of magnitude faster than the single-CPU version.
PyMF performs spatial filtering (matched filter, matched multifilter, constrained matched filter and constrained matched mutifilter) image processing that provides optimal reduction of the contamination introduced by sources that can be approximated by templates. These techniques use the flat-sky approximation.
The Limited Post-Newtonian N-body code (LPNN) simulates post-Newtonian interactions between a massive object and many low-mass objects. The interaction between one massive object and low-mass objects is calculated by post-Newtonian approximation, and the interaction between low-mass objects is calculated by Newtonian gravity. This code is based on the sticky9 code, and can be accelerated with the use of GPU in a CUDA (version 4.2 or earlier) environment.
Supernova Time Delays (SNTD) simulates and measures time delay of multiply-imaged supernovae, and offers an improved characterization of the uncertainty caused by microlensing. Lensing time delays can be determined by fitting the multiple light curves of these objects; measuring these delays provide precise tests of lens models or constraints on the Hubble constant and other cosmological parameters that are independent of the local distance ladder. Fitting the effects of microlensing without an accurate prior often leads to biases in the time delay measurement and over-fitting to the data; this can be mitigated by using a Gaussian Process Regression (GPR) technique to determine the uncertainty due to microlensing. SNTD can produce accurate simulations for wide-field time domain surveys such as LSST and WFIRST.
stellarWakes uses stellar kinematic data to search for dark matter (DM) subhalos through their gravitational perturbations to the stellar phase-space distribution.
Bilby provides a user-friendly interface to perform parameter estimation. It is primarily designed and built for inference of compact binary coalescence events in interferometric data, such as analysis of compact binary mergers and other types of signal model including supernovae and the remnants of binary neutron star mergers, but it can also be used for more general problems. The software is flexible, allowing the user to change the signal model, implement new likelihood functions, and add new detectors. Bilby can also be used to do population studies using hierarchical Bayesian modelling.
The Python suite eddy recovers precise rotation profiles of protoplanetary disks from Doppler shifted line emission, providing an easy way to fit first moment maps and the inference of a rotation velocity from an annulus of spectra.
bettermoments measures precise line-of-sight velocities from Doppler shifted lines to determine small scale deviations indicative of, for example, embedded planets.
SEDobs uses state-of-the-art theoretical galaxy SEDs (spectral energy distributions) to create simulated observations of distant galaxies. It used BC03 and M05 theoretical models and allows the user to configure the simulated observation that are needed. For a given simulated galaxy, the user is able to simulate multi-spectral and multi-photometric observations.
Photon makes simple 1D plots in python. It uses mainly matplotlib and PyQt5 and has been build to be fully customizable, allowing the user to change the fontstyle, fontsize, fontcolors, linewidth of the axes, thickness, and other parameters, and see the changes directly in the plot. Once a customization is created, it can be saved in a configuration file and reloaded for future use, allowing reuse of the customization for other plots. The main tool is a graphical user interface and it is started using a command line interface.
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.
Galaxia_wrap is a python wrap around the popular Galaxia tool (ascl:1101.007) for generating mock stellar surveys, such as a magnitude limited survey, using a built-in Galaxy model or directly from n-body data. It also offers n-body functionality and has been used to infer the age distribution of a specific stellar tracer population.
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