Results 201-250 of 2160 (2122 ASCL, 38 submitted)
THALASSA (Tool for High-Accuracy, Long-term Analyses for SSA) propagates orbits for bodies in the Earth-Moon-Sun system. Written in Fortran, it integrates either Newtonian equations in Cartesian coordinates or regularized equations of motion with the LSODAR (Livermore Solver for Ordinary Differential equations with Automatic Root-finding). THALASSA is a command-line tool; the repository also includes some Python3 scripts to perform batch propagations.
LensQuEst forecasts the signal-to-noise of CMB lensing estimators (standard, shear-only, magnification-only), generates mock maps, lenses them, and applies various lensing estimators to them. It can manipulate flat sky maps in various ways, including FFT, filtering, power spectrum, generating Gaussian random field, and applying lensing to a map, and evaluate these estimators on flat sky maps.
The LensCNN (Convolutional Neural Network) identifies images containing gravitational lensing systems after being trained and tested on simulated images, recovering most systems that are identifiable by eye.
rPICARD (Radboud PIpeline for the Calibration of high Angular Resolution Data) reduces data from different VLBI arrays, including high-frequency and low-sensitivity arrays, and supports continuum, polarization, and phase-referencing observations. Built on the CASA (ascl:1107.013) framework, it uses CASA for CLEAN imaging and self-calibration, and can be run non-interactively after only a few non-default input parameters are set. rPICARD delivers high-quality calibrated data and large bandwidth data can be processed within reasonable computing times.
Bandmerge takes in ASCII tables of positions and fluxes of detected astronomical sources in 2-7 different wavebands, and write out a single table of the merged data. The tool was designed to work with source lists generated by the Spitzer Science Center's MOPEX software, although it can be "fooled" into running on other data as well.
SPARK (Software Package for Astronomical Reduction with KMOS) reduces data from the K-band Multi Object Spectrograph (KMOS) for the VLT. In many cases, science data can be processed using a single recipe; alternately, all functions this recipe provides can be performed using other recipes provided as tools. Among the functions the recipes provide are sky subtraction, cube reconstruction with the application of flexure corrections, dividing out the telluric spectrum, applying an illumination correction, aligning the cubes, and then combinging them. The result is a set of files which contain the combined datacube and associated noise cube for each of the 24 integral field unit (IFUs). The pipeline includes simple error propagation.
Fitsverify rigorously checks whether a FITS (Flexible Image Transport System) data file conforms to the requirements defined in Version 3.0 of the FITS Standard document; it is a standalone version of the ftverify and fverify tasks that are distributed as part of the ftools (ascl:9912.002) software package. The source code must be compiled and linked with the CFITSIO (ascl:1010.001) library. An interactive web is also available that can verify the format of any FITS data file on a local computer or on the Web.
Fermi Science Tools is a suite of tools for the analysis of both the Large-Area Telescope (LAT) and the Gamma-ray Burst Monitor (GBM) data, including point source analysis for generating maps, spectra, and light curves, pulsar timing analysis, and source identification.
FastPM solves the gravity Possion equation with a boosted particle mesh. Arbitrary time steps can be used. The code is intended to study the formation of large scale structure and supports plain PM and Comoving-Lagranian (COLA) solvers. A broadband correction enforces the linear theory model growth factor at large scale. FastPM scales extremely well to hundred thousand MPI ranks, which is possible through the use of the PFFT Fourier Transform library. The size of mesh in FastPM can vary with time, allowing one to use coarse force mesh at high redshift with increase temporal resolution for accurate large scale modes. The code supports a variety of Greens function and differentiation kernels, though for most practical simulations the choice of kernels does not make a difference. A parameter file interpreter is provided to validate and execute the configuration files without running the simulation, allowing creative usages of the configuration files.
HAOS-DIPER works with and manipulates data for neutral atoms and atomic ions to understand radiation emitted by some space plasmas, notably the solar atmosphere and stellar atmospheres. HAOS-DIPER works with quantum numbers for atomic levels, enabling it to perform tasks otherwise difficult or very tedious, including a variety of data checks, calculations based upon the atomic numbers, and searching and manipulating data based upon these quantum numbers. HAOS-DIPER handles conditions from LTE to coronal-like conditions, in a manner controlled by one system variable !REGIME, and has some capability for estimating data for which no accurate parameters are available and for accounting for the effects of missing atomic levels.
Q3C (Quad Tree Cube) enables fast cone, ellipse and polygonal searches and cross-matches between large astronomical catalogs inside a PostgreSQL database. The package supports searches even if objects have proper motions.
The Transiting Exoplanet Survey Satellite (TESS) produces Full Frame Images (FFIs) at a half hour cadence and keeps the same pointing for ~27 days at a time. Astrocut performs the same cutout across all FFIs that share a common pointing to create a time series of images on a small portion of the sky.
The Astrocut package has two parts: the CubeFactory and the CutoutFactory. The CubeFactory class creates a large image cube from a list of FFI files, which allows the cutout operation to be performed efficiently. The CutoutFactory class performs the actual cutout and builds a target pixel file (TPF) that is compatible with TESS pipeline TPFs. Because this software operates on TESS mission-produced FFIs, the resulting TPFs are not background-subtracted. In addition to the Astrocut software itself, the Mikulski Archive for Space Telescopes (MAST) provides a cutout service, TESScut, which runs Astrocut on MAST servers, and allows users to simply request cutouts through a web form or direct HTTP API query.
MiraPy is a Python package for problem-solving in astronomy using Deep Learning for astrophysicist, researchers and students. Current applications of MiraPy are X-Ray Binary classification, ATLAS variable star feature classification, OGLE variable star light-curve classification, HTRU1 dataset classification and Astronomical image reconstruction using encoder-decoder network. It also contains modules for loading various datasets, curve-fitting, visualization and other utilities. It is built using Keras for developing ML models to run on CPU and GPU seamlessly.
beamModelTester enables evaluation of models of the variation in sensitivity and apparent polarization of fixed antenna phased array radio telescopes. The sensitivity of such instruments varies with respect to the orientation of the source to the antenna, resulting in variation in sensitivity over altitude and azimuth that is not consistent with respect to frequency due to other geometric effects. In addition, the different relative orientation of orthogonal pairs of linear antennae produces a difference in sensitivity between the antennae, leading to an artificial apparent polarization. Comparing the model with observations made using the given telescope makes it possible evaluate the model's performance; the results of this evaluation can provide a figure of merit for the model and guide improvements to it. This system also enables plotting of results from a single station observation on a variety of parameters.
The MMIRS data reduction pipeline provides complete and flexible data reduction for long-slit and multi-slit spectroscopic observations collected using the MMT and Magellan Infrared Spectrograph (MMIRS). Written in IDL, it offers sky subtraction, correction for telluric absorpition, and is fast enough to permit real-time data reduction for quality control.
Binospec reduces data for the Binospec imaging spectrograph. The software is also used for observation planning and instrument control, and is automated to decrease the number of tasks the user has to perform. Binospec uses a database-driven approach for instrument configuration and sequencing of observations to maximize efficiency, and a web-based interface is available for defining observations, monitoring status, and retrieving data products.
evolstate assigns crude evolutionary states (main-sequence, subgiant, red giant) to stars given an input temperature and radius/surface gravity, based on physically motivated boundaries from solar metallicity interior models.
Py4CAtS (PYthon scripts for Computational ATmospheric Spectroscopy) implements the individual steps of an infrared or microwave radiative transfer computation in separate scripts (and corresponding functions) to extract lines of relevant molecules in the spectral range of interest, compute line-by-line cross sections for given pressure(s) and temperature(s), combine cross sections to absorption coefficients and optical depths, and integrate along the line-of-sight to transmission and radiance/intensity. The code is a Python re-implementation of the Fortran code GARLIC (Generic Atmospheric Radiation Line-by-line Code) and uses the Numeric/Scientific Python modules for computationally-intensive highly optimized array-processing. Py4CAtS can be used in the console/terminal, inside the (I)Python interpreter, and in Jupyter notebooks.
Grizli produces quantitative and comprehensive modeling and fitting of slitless spectroscopic observations, which typically involve overlapping spectra of hundreds or thousands of objects in exposures taken with one or more separate grisms and at multiple dispersion position angles. This type of analysis provides complete and uniform characterization of the spectral properties (e.g., continuum shape, redshifts, line fluxes) of all objects in a given exposure taken in the slitless spectroscopic mode.
nudec_BSM uses a simplified approach to solve for the neutrino decoupling, allowing one to capture the time dependence of the process while accounting for all possible interactions that can alter it.
JVarStar (Java Variable Star Analysis) performs pattern classification by analyzing variable star data. This all-in-one library package includes machine learning techniques, fundamental mathematical methods, and digital signal processing functions that can be externally referenced (i.e., from Python), or can be used for further Java development. This library has dependencies on several open source packages that, along with the developed functionality, provides a developer with an easily accessible library from which to construct stable variable star analysis and classification code.
covdisc computes the disconnected part of the covariance matrix of 2-point functions in large-scale structure studies, accounting for the survey window effect. This method works for both power spectrum and correlation function, and applies to the covariances for various probes including the multi- poles and the wedges of 3D clustering, the angular and the projected statistics of clustering and lensing, as well as their cross covariances.
nbodykit provides algorithms for analyzing cosmological datasets from N-body simulations and large-scale structure surveys, and takes advantage of the abundance and availability of large-scale computing resources. The package provides a unified treatment of simulation and observational datasets by insulating algorithms from data containers, and reduces wall-clock time by scaling to thousands of cores. All algorithms are parallel and run with Message Passing Interface (MPI); the code is designed to be deployed on large super-computing facilities. nbodykit offers an interactive user interface that performs as well in a Jupyter notebook as on super-computing machines.
pyRSD computes the theoretical predictions of the redshift-space power spectrum of galaxies. It also includes functionality for fitting data measurements and finding the optimal model parameters, using both MCMC and nonlinear optimization techniques.
Properimage processes astronomical image; it is specially written for coaddition and image subtraction. It performs the statistical proper-coadd of several images using a spatially variant PSF estimation, and also difference image analysis by several strategies developed by others. Most of the code is based on a class called SingleImage, which provides methods and properties for image processing such as PSF determination.
OoT (Out-of-Transit) calculates the light curves and radial velocity signals due to a planet orbiting a star. It explicitly models the effects of tides, orbital motion. relativistic beaming, and reflection of the stars light by the planet. The code can also be used to model secondary eclipses.
digest2 classifies Near-Earth Object (NEO) candidates by providing a score, D2, that represents a pseudo-probability that a tracklet belongs to a given solar system orbit type. The code accurately and precisely distinguishes NEOs from non-NEOs, thus helping to identify those to be prioritized for follow-up observation. This fast, short-arc orbit classifier for small solar system bodies code is built upon the Pangloss code developed by Robert McNaught and further developed by Carl Hergenrother and Tim Spahr and Robert Jedicke's 223.f code.
eleanor extracts target pixel files from TESS Full Frame Images and produces systematics-corrected light curves for any star observed by the TESS mission. eleanor takes a TIC ID, a Gaia source ID, or (RA, Dec) coordinates of a star observed by TESS and returns, as a single object, a light curve and accompanying target pixel data. The process can be customized, allowing, for example, examination of intermediate data products and changing the aperture used for light curve extraction. eleanor also offers tools that make it easier to work with stars observed in multiple TESS sectors.
TP2VIS creates visibilities from a single dish cube; the TP visibilities can be combined with the interferometric visibilities in a joint deconvolution using, for example, CASA's tclean() method. TP2VIS requires CASA 5.4 (ascl:1107.013) or above.
SARAH builds and analyzes SUSY and non-SUSY models. It calculates all vertices, mass matrices, tadpoles equations, one-loop corrections for tadpoles and self-energies, and two-loop RGEs for a given model. SARAH writes model files for a variety of other software packages for dark matter studies, includes many SUSY and non-SUSY models, and makes implementing new models efficient and straightforward. Written in Mathematica, SARAH can also use output from Vevacious (ascl:1904.019) to check for the global minimum for a given model and parameter point.
Vevacious takes a generic expression for a one-loop effective potential energy function and finds all the tree-level extrema, which are then used as the starting points for gradient-based minimization of the one-loop effective potential. The tunneling time from a given input vacuum to the deepest minimum, if different from the input vacuum, can be calculated. The parameter points are given as files in the SLHA format (though is not restricted to supersymmetric models), and new model files can be easily generated automatically by the Mathematica package SARAH (ascl:1904.020).
Specstack creates stacked spectra using a simple algorithm with sigma-clipping to combine the spectra of galaxies in the rest-frame into a single averaged spectrum. Though written originally for galaxy spectra, it also works for other types of objects. It is written in Python and is started from the command-line.
dfitspy searches and displays metadata contained in FITS files. Written in Python, it displays the results of a metadata search and is able to grep certain values of keywords inside large samples of files in the terminal. dfitspy can be used directly with the command line interface and can also be imported as a python module into other python code or the python interpreter.
simuTrans models transit light curves affected by gravity-darkened stars. The code defines a star on a grid by modeling the brightness of each point as blackbody emission, then sets a series of parameters and uses emcee (ascl:1303.002) to explore the posterior probability distribution for the remaining fitted parameters and determine their best-fit values.
SBGAT (Small Body Geophysical Analysis Tool) generates simulated data originating from small bodies shape models, combined with advanced shape-modification properties. It uses polyhedral shape models from which can be computed mass properties such as volume, center of mass, and inertia, synthetic observations such as lightcurves and radar, and which can be used within dynamical models, such as spherical harmonics and polyhedron gravity modeling. SBGAT can generate spherical harmonics expansions from constant-density polyhedra (and export them to JSON) and evaluate the spherical harmonics expansions. It can also generate YORP coefficients, multi-threaded Polyhedron Gravity Model gravity and potential evaluations, and synthetic light-curve and radar observations for single/primary asteroids.
SBGAT has two distinct packages: a dynamic library SBGAT Core that contains the data structure and algorithm backbone of SBGAT, and SBGAT Gui, which wraps the former inside a VTK, Qt user interface to facilitate user/data interaction. SBGAT Core can be used without the SBGAT Gui wrapper.
rate computes thermochemical-equilibrium abundances for a H-C-N-O system with known pressure, temperature, and elemental abundances. The output abundances are H2O, CH4, CO, CO2, NH3, C2H2, C2H4, HCN, and N2, H2, H, and He.
EightBitTransit calculates the light curve of any pixelated image transiting a star and inverts a light curve to recover the "shadow image" that produced it.
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.
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