Results 1651-1700 of 1955 (1928 ASCL, 27 submitted)
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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/.
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.
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.
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.
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.
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.
KSTAT calculates the 2 and 3-point correlation functions in discreet point data. These include the two-point correlation function in 2 and 3-dimensions, the anisotripic 2PCF decomposed in either sigma-pi or Kazin's dist. mu projection. The 3-point correlation function can also work in anisotropic coordinates. The code is based on kd-tree structures and is parallelized using a mixture of MPI and OpenMP.
pydftools is a pure-python port of the dftools R package (ascl:1805.002), which finds the most likely P parameters of a D-dimensional distribution function (DF) generating N objects, where each object is specified by D observables with measurement uncertainties. For instance, if the objects are galaxies, it can fit a MF (P=1), a mass-size distribution (P=2) or the mass-spin-morphology distribution (P=3). Unlike most common fitting approaches, this method accurately accounts for measurement in uncertainties and complex selection functions. Though this package imitates the dftools package quite closely while being as Pythonic as possible, it has not implemented 2D+ nor non-parametric.
powerbox creates density grids (or boxes) with an arbitrary two-point distribution (i.e. power spectrum). The software works in any number of dimensions, creates Gaussian or Log-Normal fields, and measures power spectra of output fields to ensure consistency. The primary motivation for creating the code was the simple creation of log-normal mock galaxy distributions, but the methodology can be used for other applications.
dftools, written in R, finds the most likely P parameters of a D-dimensional distribution function (DF) generating N objects, where each object is specified by D observables with measurement uncertainties. For instance, if the objects are galaxies, it can fit a mass function (D=1), a mass-size distribution (D=2) or the mass-spin-morphology distribution (D=3). Unlike most common fitting approaches, this method accurately accounts for measurement in uncertainties and complex selection functions.
lcps searches for transit-like features (i.e., dips) in photometric data. Its main purpose is to restrict large sets of light curves to a number of files that show interesting behavior, such as drops in flux. While lcps is adaptable to any format of time series, its I/O module is designed specifically for photometry of the Kepler spacecraft. It extracts the pre-conditioned PDCSAP data from light curves files created by the standard Kepler pipeline. It can also handle csv-formatted ascii files. lcps uses a sliding window technique to compare a section of flux time series with its surroundings. A dip is detected if the flux within the window is lower than a threshold fraction of the surrounding fluxes.
EARL (Exoplanet Analytic Reflected Lightcurves) computes the analytic form of a reflected lightcurve, given a spherical harmonic decomposition of the planet albedo map and the viewing and orbital geometries. The EARL Mathematica notebook allows rapid computation of reflected lightcurves, thus making lightcurve numerical experiments accessible.
3DCORE forward models solar storm magnetic flux ropes called 3-Dimensional Coronal Rope Ejection (3DCORE). The code is able to produce synthetic in situ observations of the magnetic cores of solar coronal mass ejections sweeping over planets and spacecraft. Near Earth, these data are taken currently by the Wind, ACE and DSCOVR spacecraft. Other suitable spacecraft making these kind of observations carrying magnetometers in the solar wind were MESSENGER, Venus Express, MAVEN, and even Helios.
StePS (Stereographically Projected Cosmological Simulations) compactifies the infinite spatial extent of the Universe into a finite sphere with isotropic boundary conditions to simulate the evolution of the large-scale structure. This eliminates the need for periodic boundary conditions, which are a numerical convenience unsupported by observation and which modifies the law of force on large scales in an unrealistic fashion. StePS uses stereographic projection for space compactification and naive O(N2) force calculation; this arrives at a correlation function of the same quality more quickly than standard (tree or P3M) algorithms with similar spatial and mass resolution. The N2 force calculation is easy to adapt to modern graphics cards, hence StePS can function as a high-speed prediction tool for modern large-scale surveys.
exocartographer solves the exo-cartography inverse problem. This flexible forward-modeling framework, written in Python, retrieves the albedo map and spin geometry of a planet based on time-resolved photometry; it uses a Markov chain Monte Carlo method to extract albedo maps and planet spin and their uncertainties. Gaussian Processes use the data to fit for the characteristic length scale of the map and enforce smooth maps.
The AGAMA library is a collection of tools for constructing and analyzing models of galaxies. It computes gravitational potential and forces, performs orbit integration and analysis, and can convert between position/velocity and action/angle coordinates. It offers a framework for finding best-fit parameters of a model from data and self-consistent multi-component galaxy models, and contains useful auxiliary utilities such as various mathematical routines. The core of the library is written in C++, and there are Python and Fortran interfaces. AGAMA may be used as a plugin for the stellar-dynamical software packages galpy (ascl:1411.008), AMUSE (ascl:1107.007), and NEMO (ascl:1010.051).
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.
Smoothed Particle Hydrodynamics (SPH) is a Lagrangian particle method that approximates a continuous fluid as discrete nodes, each carrying various parameters such as mass, position, velocity, pressure, and temperature. In an SPH simulation the resolution scales with the particle density; StarSmasher is able to handle both equal-mass and equal number-density particle models. StarSmasher solves for hydro forces by calculating the pressure for each particle as a function of the particle's properties - density, internal energy, and internal properties (e.g. temperature and mean molecular weight). The code implements variational equations of motion and libraries to calculate the gravitational forces between particles using direct summation on NVIDIA graphics cards. Using a direct summation instead of a tree-based algorithm for gravity increases the accuracy of the gravity calculations at the cost of speed. The code uses a cubic spline for the smoothing kernel and an artificial viscosity prescription coupled with a Balsara Switch to prevent unphysical interparticle penetration. The code also implements an artificial relaxation force to the equations of motion to add a drag term to the calculated accelerations during relaxation integrations. Initially called StarCrash, StarSmasher was developed originally by Rasio.
PoMiN is a lightweight N-body code based on the Post-Minkowskian N-body Hamiltonian of Ledvinka, Schafer, and Bicak, which includes General Relativistic effects up to first order in Newton's constant G, and all orders in the speed of light c. PoMiN is a single file written in C and uses a fourth-order Runge-Kutta integration scheme. PoMiN has also been written to handle an arbitrary number of particles (both massive and massless) with a computational complexity that scales as O(N^2).
Arcmancer computes geodesics and performs polarized radiative transfer in user-specified spacetimes. The library supports Riemannian and semi-Riemannian spaces of any dimension and metric; it also supports multiple simultaneous coordinate charts, embedded geometric shapes, local coordinate systems, and automatic parallel propagation. Arcmancer can be used to solve various problems in numerical geometry, such as solving the curve equation of motion using adaptive integration with configurable tolerances and differential equations along precomputed curves. It also provides support for curves with an arbitrary acceleration term and generic tools for generating ray initial conditions and performing parallel computation over the image, among other tools.
Comparing properties of discovered trans-Neptunian Objects (TNOs) with dynamical models is impossible due to the observational biases that exist in surveys. The OSSOS Survey Simulator takes an intrinsic orbital model (from, for example, the output of a dynamical Kuiper belt emplacement simulation) and applies the survey biases, so the biased simulated objects can be directly compared with real discoveries.
BinMag examines theoretical stellar spectra computed with Synth/SynthMag/Synmast/Synth3/SME spectrum synthesis codes and compare them to observations. An IDL widget program, BinMag applies radial velocity shift and broadening to the theoretical spectra to account for the effects of stellar rotation, radial-tangential macroturbulence, instrumental smearing. The code can also simulate spectra of spectroscopic binary stars by appropriate coaddition of two synthetic spectra. Additionally, BinMag can be used to measure equivalent width, fit line profile shapes with analytical functions, and to automatically determine radial velocity and broadening parameters. BinMag interfaces with the Synth3 (ascl:1212.010) and SME (ascl:1202.013) codes, allowing the user to determine chemical abundances and stellar atmospheric parameters from the observed spectra.
XSPEC_EMCEE is an XSPEC-friendly interface for emcee (ascl:1303.002). It carries out MCMC analyses of X-ray spectra in the X-ray spectral fitting program XSPEC (ascl:9910.005). It can run multiple xspec processes simultaneously, speeding up the analysis, and can switch to parameterizing norm
parameters in log space.
SNSEDextend extrapolates core-collapse and Type Ia Spectral Energy Distributions (SEDs) into the UV and IR for use in simulations and photometric classifications. The user provides a library of existing SED templates (such as those in the authors' SN SED Repository) along with new photometric constraints in the UV and/or NIR wavelength ranges. The software then extends the existing template SEDs so their colors match the input data at all phases. SNSEDextend can also extend the SALT2 spectral time-series model for Type Ia SN for a "first-order" extrapolation of the SALT2 model components, suitable for use in survey simulations and photometric classification tools; as the code does not do a rigorous re-training of the SALT2 model, the results should not be relied on for precision applications such as light curve fitting for cosmology.
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