Results 201-250 of 1882 (1851 ASCL, 31 submitted)
FastChem is an equilibrium chemistry code that calculates the chemical composition of the gas phase for given temperatures and pressures. Written in C++, it is based on a semi-analytic approach, and is optimized for extremely fast and accurate calculations.
LFlGRB models the luminosity function (LF) of long Gamma Ray Bursts (lGRBs) by using a sample of Swift and Fermi lGRBs to re-derive the parameters of the Yonetoku correlation and self-consistently estimate pseudo-redshifts of all the bursts with unknown redshifts. The GRB formation rate is modeled as the product of the cosmic star formation rate and a GRB formation efficiency for a given stellar mass.
LFsGRB models the luminosity function (LF) of short Gamma Ray Bursts (sGRBs) by using the available catalog data of all short GRBs (sGRBs) detected till 2017 October, estimating the luminosities via pseudo-redshifts obtained from the Yonetoku correlation, and then assuming a standard delay distribution between the cosmic star formation rate and the production rate of their progenitors. The data are fit well both by exponential cutoff powerlaw and broken powerlaw models. Using the derived parameters of these models along with conservative values in the jet opening angles seen from afterglow observations, the true rate of short GRBs is derived. Assuming a short GRB is produced from each binary neutron star merger (BNSM), the rate of gravitational wave (GW) detections from these mergers are derived for the past, present and future configurations of the GW detector networks.
UniDAM obtains a homogenized set of stellar parameters from spectrophotometric data of different surveys. Parallax and extinction data can be incorporated into the isochrone fitting method used in UniDAM to reduce distance and age estimate uncertainties for TGAS stars for distances up to 1 kpc and decrease distance Gaia end-of-mission parallax uncertainties by about a factor of 20 and age uncertainties by a factor of two for stars up to 10 kpc away from the Sun.
allantools calculates Allan deviation and related time & frequency statistics. The library is written in Python and has a GPL v3+ license. It takes input data that is either evenly spaced observations of either fractional frequency, or phase in seconds. Deviations are calculated for given tau values in seconds. Several noise generators for creating synthetic datasets are also included.
Agatha is a framework of periodograms to disentangle periodic signals from correlated noise and to solve the two-dimensional model selection problem: signal dimension and noise model dimension. These periodograms are calculated by applying likelihood maximization and marginalization and combined in a self-consistent way. Agatha can be used to select the optimal noise model and to test the consistency of signals in time and can be applied to time series analyses in other astronomical and scientific disciplines. An interactive web implementation of the software is also available at http://agatha.herts.ac.uk/.
ViSBARD interactively visualizes and analyzes space physics data. It provides an interactive integrated 3-D and 2-D environment to determine correlations between measurements across many spacecraft. It supports a variety of spacecraft data products and MHD models and is easily extensible to others. ViSBARD provides a way of visualizing multiple vector and scalar quantities as measured by many spacecraft at once. The data are displayed three-dimesionally along the orbits which may be displayed either as connected lines or as points. The data display allows the rapid determination of vector configurations, correlations between many measurements at multiple points, and global relationships. With the addition of magnetohydrodynamic (MHD) model data, this environment can also be used to validate simulation results with observed data, use simulated data to provide a global context for sparse observed data, and apply feature detection techniques to the simulated data.
3DView creates visualizations of space physics data in their original 3D context. Time series, vectors, dynamic spectra, celestial body maps, magnetic field or flow lines, and 2D cuts in simulation cubes are among the variety of data representation enabled by 3DView. It offers direct connections to several large databases and uses VO standards; it also allows the user to upload data. 3DView's versatility covers a wide range of space physics contexts.
APPHi (Automated Photometry Pipeline) carries out aperture and differential photometry of TAOS-II project data. It is computationally efficient and can be used also with other astronomical wide-field image data. APPHi works with large volumes of data and handles both FITS and HDF5 formats. Due the large number of stars that the software has to handle in an enormous number of frames, it is optimized to automatically find the best value for parameters to carry out the photometry, such as mask size for aperture, size of window for extraction of a single star, and the number of counts for the threshold for detecting a faint star. Although intended to work with TAOS-II data, APPHi can analyze any set of astronomical images and is a robust and versatile tool to performing stellar aperture and differential photometry.
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.
NR-code applies nonlinear reconstruction to the dark matter density field in redshift space and solves for the nonlinear mapping from the initial Lagrangian positions to the final redshift space positions; this reverses the large-scale bulk flows and improves the precision measurement of the baryon acoustic oscillations (BAO) scale.
This software trains artificial neural networks to find non-linear functionals of data that maximize Fisher information: information maximizing neural networks (IMNNs). As compressing large data sets vastly simplifies both frequentist and Bayesian inference, important information may be inadvertently missed. Likelihood-free inference based on automatically derived IMNN summaries produces summaries that are good approximations to sufficient statistics. IMNNs are robustly capable of automatically finding optimal, non-linear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise, inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra, and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima.
CAT-PUMA (CME Arrival Time Prediction Using Machine learning Algorithms) quickly and accurately predicts the arrival of Coronal Mass Ejections (CMEs) of CME arrival time. The software was trained via detailed analysis of CME features and solar wind parameters using 182 previously observed geo-effective partial-/full-halo CMEs and uses algorithms of the Support Vector Machine (SVM) to make its predictions, which can be made within minutes of providing the necessary input parameters of a CME.
Lenstronomy is a multi-purpose open-source gravitational lens modeling python package. Lenstronomy reconstructs the lens mass and surface brightness distributions of strong lensing systems using forward modelling and supports a wide range of analytic lens and light models in arbitrary combination. The software is also able to reconstruct complex extended sources as well as point sources. Lenstronomy is flexible and numerically accurate, with a clear user interface that could be deployed across different platforms. Lenstronomy has been used to derive constraints on dark matter properties in strong lenses, measure the expansion history of the universe with time-delay cosmography, measure cosmic shear with Einstein rings, and decompose quasar and host galaxy light.
The DESCQA framework provides rigorous validation protocols for assessing the quality of high-quality simulated sky catalogs in a straightforward and comprehensive way. DESCQA enables the inspection, validation, and comparison of an inhomogeneous set of synthetic catalogs via the provision of a common interface within an automated framework. An interactive web interface is also available at portal.nersc.gov/project/lsst/descqa.
SMERFS (Stochastic Markov Evaluation of Random Fields on the Sphere) creates large realizations of random fields on the sphere. It uses a fast algorithm based on Markov properties and fast Fourier Transforms in 1d that generates samples on an n X n grid in O(n2 log n) and efficiently derives the necessary conditional covariance matrices.
orbit-estimation tests and evaluates the Stäckel approximation method for estimating orbit parameters in galactic potentials. It relies on the approximation of the Galactic potential as a Stäckel potential, in a prolate confocal coordinate system, under which the vertical and horizontal motions decouple. By solving the Hamilton Jacobi equations at the turning points of the horizontal and vertical motions, it is possible to determine the spatial boundary of the orbit, and hence calculate the desired orbit parameters.
The Empirical Galaxy Generator (EGG) generates fake galaxy catalogs and images with realistic positions, morphologies and fluxes from the far-ultraviolet to the far-infrared. The catalogs are generated by egg-gencat and stored in binary FITS tables (column oriented). Another program, egg-2skymaker, is used to convert the generated catalog into ASCII tables suitable for ingestion by SkyMaker (ascl:1010.066) to produce realistic high resolution images (e.g., Hubble-like), while egg-gennoise and egg-genmap can be used to generate the low resolution images (e.g., Herschel-like). These tools can be used to test source extraction codes, or to evaluate the reliability of any map-based science (stacking, dropout identification, etc.).
Chroma investigates biases originating from two chromatic effects in the atmosphere: differential chromatic refraction (DCR), and wavelength dependence of seeing. These biases arise when using the point spread function (PSF) measured with stars to estimate the shapes of galaxies with different spectral energy distributions (SEDs) than the stars.
ProFound detects sources in noisy images, generates segmentation maps identifying the pixels belonging to each source, and measures statistics like flux, size, and ellipticity. These inputs are key requirements of ProFit (ascl:1612.004), our galaxy profiling package; these two packages used in unison semi-automatically profile large samples of galaxies. The key novel feature introduced in ProFound is that all photometry is executed on dilated segmentation maps that fully contain the identifiable flux, rather than using more traditional circular or ellipse-based photometry. Also, to be less sensitive to pathological segmentation issues, the de-blending is made across saddle points in flux. ProFound offers good initial parameter estimation for ProFit, and also segmentation maps that follow the sometimes complex geometry of resolved sources, whilst capturing nearly all of the flux. A number of bulge-disc decomposition projects are already making use of the ProFound and ProFit pipeline.
DaCHS, the Data Center Helper Suite, is an integrated package for publishing astronomical data sets to the Virtual Observatory. Network-facing, it speaks the major VO protocols (SCS, SIAP, SSAP, TAP, Datalink, etc). Operator-facing, many input formats, including FITS/WCS, ASCII files, and VOTable, can be processed to publication-ready data. DaCHS puts particular emphasis on integrated metadata handling, which facilitates a tight integration with the VO's Registry
AstroCV processes and analyzes big astronomical datasets, and is intended to provide a community repository of high performance Python and C++ algorithms used for image processing and computer vision. The library offers methods for object recognition, segmentation and classification, with emphasis in the automatic detection and classification of galaxies.
DPPP (Default Pre-Processing Pipeline, also referred to as NDPPP) reads and writes radio-interferometric data in the form of Measurement Sets, mainly those that are created by the LOFAR telescope. It goes through visibilities in time order and contains standard operations like averaging, phase-shifting and flagging bad stations. Between the steps in a pipeline, the data is not written to disk, making this tool suitable for operations where I/O dominates. More advanced procedures such as gain calibration are also included. Other computing steps can be provided by loading a shared library; currently supported external steps are the AOFlagger (ascl:1010.017) and a bridge that enables loading python steps.
ipole is a ray-tracing code for covariant, polarized radiative transport particularly useful for modeling Event Horizon Telescope sources, though may also be used for other relativistic transport problems. The code extends the ibothros scheme for covariant, unpolarized transport using two representations of the polarized radiation field: in the coordinate frame, it parallel transports the coherency tensor, and in the frame of the plasma, it evolves the Stokes parameters under emission, absorption, and Faraday conversion. The transport step is as spacetime- and coordinate- independent as possible; the emission, absorption, and Faraday conversion step is implemented using an analytic solution to the polarized transport equation with constant coefficients. As a result, ipole is stable, efficient, and produces a physically reasonable solution even for a step with high optical depth and Faraday depth.
ASERA, ASpectrum Eye Recognition Assistant, aids in quasar spectral recognition and redshift measurement and can also be used to recognize various types of spectra of stars, galaxies and AGNs (Active Galactic Nucleus). This interactive software allows users to visualize observed spectra, superimpose template spectra from the Sloan Digital Sky Survey (SDSS), and interactively access related spectral line information. ASERA is an efficient and user-friendly semi-automated toolkit for the accurate classification of spectra observed by LAMOST (the Large Sky Area Multi-object Fiber Spectroscopic Telescope) and is available as a standalone Java application and as a Java applet. The software offers several functions, including wavelength and flux scale settings, zoom in and out, redshift estimation, and spectral line identification.
RAPTOR produces accurate images, animations, and spectra of relativistic plasmas in strong gravity by numerically integrating the equations of motion of light rays and performing time-dependent radiative transfer calculations along the rays. The code is compatible with any analytical or numerical spacetime, is hardware-agnostic and may be compiled and run on both GPUs and CPUs. RAPTOR is useful for studying accretion models of supermassive black holes, performing time-dependent radiative transfer through general relativistic magneto-hydrodynamical (GRMHD) simulations and investigating the expected observational differences between the so-called fastlight and slow-light paradigms.
ExoCross generates spectra and thermodynamic properties from molecular line lists in ExoMol, HITRAN, or several other formats. The code is parallelized and also shows a high degree of vectorization; it works with line profiles such as Doppler, Lorentzian and Voigt and supports several broadening schemes. ExoCross is also capable of working with the recently proposed method of super-lines. It supports calculations of lifetimes, cooling functions, specific heats and other properties. ExoCross converts between different formats, such as HITRAN, ExoMol and Phoenix, and simulates non-LTE spectra using a simple two-temperature approach. Different electronic, vibronic or vibrational bands can be simulated separately using an efficient filtering scheme based on the quantum numbers.
optBINS (optimal binning) determines the optimal number of bins in a uniform bin-width histogram by deriving the posterior probability for the number of bins in a piecewise-constant density model after assigning a multinomial likelihood and a non-informative prior. The maximum of the posterior probability occurs at a point where the prior probability and the the joint likelihood are balanced. The interplay between these opposing factors effectively implements Occam's razor by selecting the most simple model that best describes the data.
Long Wavelength Propagation Capability (LWPC), written as a collection of separate programs that perform unique actions, generates geographical maps of signal availability for coverage analysis. The program makes it easy to set up these displays by automating most of the required steps. The user specifies the transmitter location and frequency, the orientation of the transmitting and receiving antennae, and the boundaries of the operating area. The program automatically selects paths along geographic bearing angles to ensure that the operating area is fully covered. The diurnal conditions and other relevant geophysical parameters are then determined along each path. After the mode parameters along each path are determined, the signal strength along each path is computed. The signal strength along the paths is then interpolated onto a grid overlying the operating area. The final grid of signal strength values is used to display the signal-strength in a geographic display. The LWPC uses character strings to control programs and to specify options. The control strings have the same meaning and use among all the programs.
ExtLaw_H18 generates the extinction law between 0.8 - 2.2 microns. The law is derived using the Westerlund 1 (Wd1) main sequence (A_Ks ~ 0.6 mag) and Arches cluster field Red Clump at the Galactic Center (A_Ks ~ 2.7 mag). To derive the law a Wd1 cluster age of 5 Myr is assumed, though changing the cluster age between 4 Myr -- 7 Myr has no effect on the law. This extinction law can be applied to highly reddened stellar populations that have similar foreground material as Wd1 and the Arches RC, namely dust from the spiral arms of the Milky Way in the Galactic Plane.
3D-PDR is a three-dimensional photodissociation region code written in Fortran. It uses the Sundials package (written in C) to solve the set of ordinary differential equations and it is the successor of the one-dimensional PDR code UCL_PDR (ascl:1303.004). Using the HEALpix ray-tracing scheme (ascl:1107.018), 3D-PDR solves a three-dimensional escape probability routine and evaluates the attenuation of the far-ultraviolet radiation in the PDR and the propagation of FIR/submm emission lines out of the PDR. The code is parallelized (OpenMP) and can be applied to 1D and 3D problems.
The SETI Encryption code, written in Python, creates a message for use in testing the decryptability of a simulated incoming interstellar message. The code uses images in a portable bit map (PBM) format, then writes the corresponding bits into the message, and finally returns both a PBM image and a text (TXT) file of the entire message. The natural constants (c, G, h) and the wavelength of the message are defined in the first few lines of the code, followed by the reading of the input files and their conversion into 757 strings of 359 bits to give one page. Each header of a page, i.e. the little-endian binary code translation of the tempo-spatial yardstick, is calculated and written on-the-fly for each page.
FAST (Fitting and Assessment of Synthetic Templates) fits stellar population synthesis templates to broadband photometry and/or spectra. FAST is compatible with the photometric redshift code EAzY (ascl:1010.052) when fitting broadband photometry; it uses the photometric redshifts derived by EAzY, and the input files (for examply, photometric catalog and master filter file) are the same. FAST fits spectra in combination with broadband photometric data points or simultaneously fits two components, allowing for an AGN contribution in addition to the host galaxy light. Depending on the input parameters, FAST outputs the best-fit redshift, age, dust content, star formation timescale, metallicity, stellar mass, star formation rate (SFR), and their confidence intervals. Though some of FAST's functions overlap with those of HYPERZ (ascl:1108.010), it differs by fitting fluxes instead of magnitudes, allows the user to completely define the grid of input stellar population parameters and easily input photometric redshifts and their confidence intervals, and calculates calibrated confidence intervals for all parameters. Note that FAST is not a photometric redshift code, though it can be used as one.
IMAGINE (Interstellar MAGnetic field INference Engine) performs inference on generic parametric models of the Galaxy. The modular open source framework uses highly optimized tools and technology such as the MultiNest sampler (ascl:1109.006) and the information field theory framework NIFTy (ascl:1302.013) to create an instance of the Milky Way based on a set of parameters for physical observables, using Bayesian statistics to judge the mismatch between measured data and model prediction. The flexibility of the IMAGINE framework allows for simple refitting for newly available data sets and makes state-of-the-art Bayesian methods easily accessible particularly for random components of the Galactic magnetic field.
MulensModel calculates light curves of microlensing events. Both single and binary lens events are modeled and various higher-order effects can be included: extended source (with limb-darkening), annual microlensing parallax, and satellite microlensing parallax. The code is object-oriented and written in Python3, and requires AstroPy (ascl:1304.002).
Kadenza enables time-critical data analyses to be carried out using NASA's Kepler Space Telescope. It enables users to convert Kepler's raw data files into user-friendly Target Pixel Files upon downlink from the spacecraft. The primary motivation for this tool is to enable the microlensing, supernova, and exoplanet communities to create quicklook lightcurves for transient events which require rapid follow-up.
nanopipe is a data reduction pipeline for calibration, RFI removal, and pulse time-of-arrival measurement from radio pulsar data. It was developed primarily for use by the NANOGrav project. nanopipe is written in Python, and depends on the PSRCHIVE (ascl:1105.014) library.
SCARLET performs source separation (aka "deblending") on multi-band images. It is geared towards optical astronomy, where scenes are composed of stars and galaxies, but it is straightforward to apply it to other imaging data. Separation is achieved through a constrained matrix factorization, which models each source with a Spectral Energy Distribution (SED) and a non-parametric morphology, or multiple such components per source. The code performs forced photometry (with PSF matching if needed) using an optimal weight function given by the signal-to-noise weighted morphology across bands. The approach works well if the sources in the scene have different colors and can be further strengthened by imposing various additional constraints/priors on each source. Because of its generic utility, this package provides a stand-alone implementation that contains the core components of the source separation algorithm. However, the development of this package is part of the LSST Science Pipeline; the meas_deblender package contains a wrapper to implement the algorithms here for the LSST stack.
CIFOG is a versatile MPI-parallelised semi-numerical tool to perform simulations of the Epoch of Reionization. From a set of evolving cosmological gas density and ionizing emissivity fields, it computes the time and spatially dependent ionization of neutral hydrogen (HI), neutral (HeI) and singly ionized helium (HeII) in the intergalactic medium (IGM). The code accounts for HII, HeII, HeIII recombinations, and provides different descriptions for the photoionization rate that are used to calculate the residual HI fraction in ionized regions. This tool has been designed to be coupled to semi-analytic galaxy formation models or hydrodynamical simulations. The modular fashion of the code allows the user to easily introduce new descriptions for recombinations and the photoionization rate.
DaMaSCUS-CRUST determines the critical cross-section for strongly interacting DM for various direct detection experiments systematically and precisely using Monte Carlo simulations of DM trajectories inside the Earth's crust, atmosphere, or any kind of shielding. Above a critical dark matter-nucleus scattering cross section, any terrestrial direct detection experiment loses sensitivity to dark matter, since the Earth crust, atmosphere, and potential shielding layers start to block off the dark matter particles. This critical cross section is commonly determined by describing the average energy loss of the dark matter particles analytically. However, this treatment overestimates the stopping power of the Earth crust; therefore, the obtained bounds should be considered as conservative. DaMaSCUS-CRUST is a modified version of DaMaSCUS (ascl:1706.003) that accounts for shielding effects and returns a precise exclusion band.
eqpair computes the electron energy distribution resulting from a balance between heating and direct acceleration of particles, and cooling processes. Electron-positron pair balance, bremstrahlung, and Compton cooling, including external soft photon input, are among the processes considered, and the final electron distribution can be hybrid, thermal, or non-thermal.
mrpy calculates the MRP parameterization of the Halo Mass Function. It calculates basic statistics of the truncated generalized gamma distribution (TGGD) with the TGGD class, including mean, mode, variance, skewness, pdf, and cdf. It generates MRP quantities with the MRP class, such as differential number counts and cumulative number counts, and offers various methods for generating normalizations. It can generate the MRP-based halo mass function as a function of physical parameters via the mrp_b13 function, and fit MRP parameters to data in the form of arbitrary curves and in the form of a sample of variates with the SimFit class. mrpy also calculates analytic hessians and jacobians at any point, and allows the user to alternate parameterizations of the same form via the reparameterize module.
collapse calculates the spherical−collapse for standard cosmological models as well as for dark energy models when the dark energy can be taken to be spatially homogeneous. The calculation is valid on sub−horizon scales and takes a top−hat perturbation to exist in an otherwise featureless cosmos and follows its evolution into the non−linear regime where it reaches a maximum size and then recollapses. collapse provides the user with the linear−collapse threshold (delta_c) and the virial overdensity (Delta_v) for the collapsed halo over a range of cosmic scale factors.
BHMcalc provides renditions of the instantaneous circumbinary habital zone (CHZ) and also calculates BHM properties of the system including those related to the rotational evolution of the stellar components and the combined XUV and SW fluxes as measured at different distances from the binary. Moreover, it provides numerical results that can be further manipulated and used to calculate other properties.
PyOSE is a fully numerical orbital sampling effect (OSE) simulator that can model arbitrary inclinations of the transiting moon orbit. It can be used to search for exomoons in long-term stellar light curves such as those by Kepler and the upcoming PLATO mission.
runDM calculates the running of the couplings of Dark Matter (DM) to the Standard Model (SM) in simplified models with vector mediators. By specifying the mass of the mediator and the couplings of the mediator to SM fields at high energy, the code can calculate the couplings at low energy, taking into account the mixing of all dimension-6 operators. runDM can also extract the operator coefficients relevant for direct detection, namely low energy couplings to up, down and strange quarks and to protons and neutrons.
Glimpse, also known as Glimpse2D, is a weak lensing mass-mapping tool that relies on a robust sparsity-based regularization scheme to recover high resolution convergence from either gravitational shear alone or from a combination of shear and flexion. Including flexion allows the supplementation of the shear on small scales in order to increase the sensitivity to substructures and the overall resolution of the convergence map. To preserve all available small scale information, Glimpse avoids any binning of the irregularly sampled input shear and flexion fields and treats the mass-mapping problem as a general ill-posed inverse problem, regularized using a multi-scale wavelet sparsity prior. The resulting algorithm incorporates redshift, reduced shear, and reduced flexion measurements for individual galaxies and is made highly efficient by the use of fast Fourier estimators.
astroplan is a flexible toolbox for observation planning and scheduling. It is powered by Astropy (ascl:1304.002); it works for Python beginners and new observers, and is powerful enough for observatories preparing nightly and long-term schedules as well. It calculates rise/set/meridian transit times, alt/az positions for targets at observatories anywhere on Earth, and offers built-in plotting convenience functions for standard observation planning plots (airmass, parallactic angle, sky maps). It can also determine the observability of sets of targets given an arbitrary set of constraints (i.e., altitude, airmass, moon separation/illumination, etc.).
AntiparticleDM calculates the prospects of future direct detection experiments to discriminate between Majorana and Dirac Dark Matter (i.e., to determine whether Dark Matter is its own antiparticle). Direct detection event rates and mock data generation are dealt with by a variation of the WIMpy code.
HiGal SED Fitter fits modified blackbody SEDs to Herschel data, specifically targeted at Herschel Hi-Gal data.
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