Results 501-550 of 2680 (2621 ASCL, 59 submitted)
ctools provides tools for the scientific analysis of Cherenkov Telescope Array (CTA) data. Analysis of data from existing Imaging Air Cherenkov Telescopes (such as H.E.S.S., MAGIC or VERITAS) is also supported, provided that the data and response functions are available in the format defined for CTA. ctools comprises a set of ftools-like binary executables with a command-line interface allowing for interactive step-wise data analysis. A Python module allows control of all executables, and the creation of shell or Python scripts and pipelines is supported. ctools provides cscripts, which are Python scripts complementing the binary executables. Extensions of the ctools package by user defined binary executables or Python scripts is supported. ctools are based on GammaLib (ascl:1110.007).
CTR (Coronal Temperature Reconstruction) reconstructs differential emission measures (DEMs) in the solar corona. Written in IDL, the code guarantees positivity of the recovered DEM, enforces an explicit smoothness constraint, returns a featureless (flat) solution in the absence of information, and converges quickly. The algorithm is robust and can be extended to other wavelengths where the DEM treatment is valid.
The Cuba library offers four independent routines for multidimensional numerical integration: Vegas, Suave, Divonne, and Cuhre. The four algorithms work by very different methods, and can integrate vector integrands and have very similar Fortran, C/C++, and Mathematica interfaces. Their invocation is very similar, making it easy to cross-check by substituting one method by another. For further safeguarding, the output is supplemented by a chi-square probability which quantifies the reliability of the error estimate.
CuBANz is a photometric redshift estimator code for high redshift galaxies that uses the back propagation neural network along with clustering of the training set, making it very efficient. The training set is divided into several self learning clusters with galaxies having similar photometric properties and spectroscopic redshifts within a given span. The clustering algorithm uses the color information (i.e. u-g, g-r etc.) rather than the apparent magnitudes at various photometric bands, as the photometric redshift is more sensitive to the flux differences between different bands rather than the actual values. The clustering method enables accurate determination of the redshifts. CuBANz considers uncertainty in the photometric measurements as well as uncertainty in the neural network training. The code is written in C.
CUBE, written in Coarray Fortran, is a particle-mesh based parallel cosmological N-body simulation code. The memory usage of CUBE can approach as low as 6 bytes per particle. Particle pairwise (PP) force, cosmological neutrinos, spherical overdensity (SO) halofinder are included.
CubeIndexer indexes regions of interest (ROIs) in data cubes reducing the necessary storage space. The software can process data cubes containing megabytes of data in fractions of a second without human supervision, thus allowing it to be incorporated into a production line for displaying objects in a virtual observatory. The software forms part of the Chilean Virtual Observatory (ChiVO) and provides the capability of content-based searches on data cubes to the astronomical community.
CUBEP3M is a high performance cosmological N-body code which has many utilities and extensions, including a runtime halo finder, a non-Gaussian initial conditions generator, a tuneable accuracy, and a system of unique particle identification. CUBEP3M is fast, has a memory imprint up to three times lower than other widely used N-body codes, and has been run on up to 20,000 cores, achieving close to ideal weak scaling even at this problem size. It is well suited and has already been used for a broad number of science applications that require either large samples of non-linear realizations or very large dark matter N-body simulations, including cosmological reionization, baryonic acoustic oscillations, weak lensing or non-Gaussian statistics.
CubiCal implements several accelerated gain solvers which exploit complex optimization for fast radio interferometric gain calibration. The code can be used for both direction-independent and direction-dependent self-calibration. CubiCal is implemented in Python and Cython, and multiprocessing is fully supported.
CUBISM, written in IDL, constructs spectral cubes, maps, and arbitrary aperture 1D spectral extractions from sets of mapping mode spectra taken with Spitzer's IRS spectrograph. CUBISM is optimized for non-sparse maps of extended objects, e.g. the nearby galaxy sample of SINGS, but can be used with data from any spectral mapping AOR (primarily validated for maps which are designed as suggested by the mapping HOWTO).
CUDAHM accelerates Bayesian inference of Hierarchical Models using Markov Chain Monte Carlo by constructing a Metropolis-within-Gibbs MCMC sampler for a three-level hierarchical model, requiring the user to supply only a minimimal amount of CUDA code. CUDAHM assumes that a set of measurements are available for a sample of objects, and that these measurements are related to an unobserved set of characteristics for each object. For example, the measurements could be the spectral energy distributions of a sample of galaxies, and the unknown characteristics could be the physical quantities of the galaxies, such as mass, distance, or age. The measured spectral energy distributions depend on the unknown physical quantities, which enables one to derive their values from the measurements. The characteristics are also assumed to be independently and identically sampled from a parent population with unknown parameters (e.g., a Normal distribution with unknown mean and variance). CUDAHM enables one to simultaneously sample the values of the characteristics and the parameters of their parent population from their joint posterior probability distribution.
cuFFS (CUDA-accelerated Fast Faraday Synthesis) performs Faraday rotation measure synthesis; it is particularly well-suited for performing RM synthesis on large datasets. Compared to a fast single-threaded and vectorized CPU implementation, depending on the structure and format of the data cubes, cuFFs achieves an increase in speed of up to two orders of magnitude. The code assumes that the pixels values are IEEE single precision floating points (BITPIX=-32), and the input cubes must have 3 axes (2 spatial dimensions and 1 frequency axis) with frequency axis as NAXIS1. A package is included to reformat data with individual stokes Q and U channel maps to the required format. The code supports both the HDFITS format and the standard FITS format, and is written in C with GPU-acceleration achieved using Nvidia's CUDA parallel computing platform.
I introduce a new code for fast calculation of the Lomb-Scargle periodogram, that leverages the computing power of graphics processing units (GPUs). After establishing a background to the newly emergent field of GPU computing, I discuss the code design and narrate key parts of its source. Benchmarking calculations indicate no significant differences in accuracy compared to an equivalent CPU-based code. However, the differences in performance are pronounced; running on a low-end GPU, the code can match 8 CPU cores, and on a high-end GPU it is faster by a factor approaching thirty. Applications of the code include analysis of long photometric time series obtained by ongoing satellite missions and upcoming ground-based monitoring facilities; and Monte-Carlo simulation of periodogram statistical properties.
The CUPID package allows the identification and analysis of clumps of emission within 1, 2 or 3 dimensional data arrays. Whilst targeted primarily at sub-mm cubes, it can be used on any regularly gridded 1, 2 or 3D data. A variety of clump finding algorithms are implemented within CUPID, including the established ClumpFind (ascl:1107.014) and GaussClumps algorithms. In addition, two new algorithms called FellWalker and Reinhold are also provided. CUPID allows easy inter-comparison between the results of different algorithms; the catalogues produced by each algorithm contains a standard set of columns containing clump peak position, clump centroid position, the integrated data value within the clump, clump volume, and the dimensions of the clump. In addition, pixel masks are produced identifying which input pixels contribute to each clump. CUPID is distributed as part of the Starlink (ascl:1110.012) software collection.
Written in c, the Customizable User Pipeline for IRS Data (CUPID) allows users to run the Spitzer IRS Pipelines to re-create Basic Calibrated Data and extract calibrated spectra from the archived raw files. CUPID provides full access to all the parameters of the BCD, COADD, BKSUB, BKSUBX, and COADDX pipelines, as well as the opportunity for users to provide their own calibration files (e.g., flats or darks). CUPID is available for Mac, Linux, and Solaris operating systems.
The CURSA package manipulates astronomical catalogs and similar tabular datasets. It provides facilities for browsing or examining catalogs; selecting subsets from a catalog; sorting and copying catalogs; pairing two catalogs; converting catalog coordinates between some celestial coordinate systems; and plotting finding charts and photometric calibration. It can also extract subsets from a catalog in a format suitable for plotting using other Starlink packages such as PONGO. CURSA can access catalogs held in the popular FITS table format, the Tab-Separated Table (TST) format or the Small Text List (STL) format. Catalogs in the STL and TST formats are simple ASCII text files. CURSA also includes some facilities for accessing remote on-line catalogs via the Internet. It is part of the Starlink software collection (ascl:1110.012).
Curvit produces light curves from UVIT (Ultraviolet Imaging Telescope) data. It uses the events list from the official UVIT L2 pipeline (version 6.3 onwards) as input. The makecurves function of curvit automatically detects sources from events list and creates light curves. Curvit provides source coordinates only in the instrument coordinate system. If you already have the source coordinates, the curve function of curvit can be used to create light curves. The package has several parameters that can be set by the user; some of these parameters have default values. Curvit is available on PyPI.
CUTE (Correlation Utilities and Two-point Estimation) extracts any two-point statistic from enormous datasets with hundreds of millions of objects, such as large galaxy surveys. The computational time grows with the square of the number of objects to be correlated; technology provides multiple means to massively parallelize this problem and CUTE is specifically designed for these kind of calculations. Two implementations are provided: one for execution on shared-memory machines using OpenMP and one that runs on graphical processing units (GPUs) using CUDA.
CuTEx analyzes images in the infrared bands and extracts sources from complex backgrounds, particularly star-forming regions that offer the challenges of crowding, having a highly spatially variable background, and having no-psf profiles such as protostars in their accreting phase. The code is composed of two main algorithms, the first an algorithm for source detection, and the second for flux extraction. The code is originally written in IDL language and it was exported in the license free GDL language. CuTEx could be used in other bands or in scientific cases different from the native case.
This software is also available as an on-line tool from the Multi-Mission Interactive Archive web pages dedicated to the Herschel Observatory.
cuvarbase provides a Python (2.7+) library for performing period finding (Lomb-Scargle, Phase Dispersion Minimization, Conditional Entropy, Box-least squares) on astronomical time-series datasets. Speedups over CPU implementations depend on the algorithm, dataset, and GPU capabilities but are typically ~1-2 orders of magnitude and are especially high for BLS and Lomb-Scargle. Unit tested and available via pip or from source at GitHub.
CVXOPT makes the development of software for convex optimization applications straightforward by building on Python’s extensive standard library and on the strengths of Python as a high-level programming language. It offers efficient Python classes for dense and sparse matrices (real and complex) with Python indexing and slicing and overloaded operations for matrix arithmetic, an interface to the fast Fourier transform routines from FFTW, and an interface to most of the double-precision real and complex BLAS. It contains routines for linear, second-order cone, and semidefinite programming problems, and for nonlinear convex optimization. CVXOPT also provides an interface to LAPACK routines for solving linear equations and least-squares problems, matrix factorizations (LU, Cholesky, LDLT and QR), symmetric eigenvalue and singular value decomposition, and Schur factorization, and a modeling tool for specifying convex piecewise-linear optimization problems.
CWITools analyzes integral field spectroscopy data from the Palomar and Keck Cosmic Web Imagers, and can be adapted for any three-dimensional integral field spectroscopy data. The package is modular, allowing users to construct data analysis pipelines to suit their own scientific needs, and includes tools for reducing data cubes, extracting a target signal, making emission maps, spectra, and other products. It also fits emission line and radial profiles and obtains final scalar quantities such as size and luminosity, among other tasks. It also contains helper functions that can, for example, obtain the wavelength axis from a 3D header, and create an auto-populated list of nebular emission lines or sky lines.
The Python module Cygrid grids (resamples) data to any collection of spherical target coordinates, although its typical application involves FITS maps or data cubes. The module supports the FITS world coordinate system (WCS) standard; its underlying algorithm is based on the convolution of the original samples with a 2D Gaussian kernel. A lookup table scheme allows parallelization of the code and is combined with the HEALPix tessellation of the sphere for fast neighbor searches. Cygrid's runtime scales between O(n) and O(nlog n), with n being the number of input samples.
D3PO (Denoising, Deconvolving, and Decomposing Photon Observations) addresses the inference problem of denoising, deconvolving, and decomposing photon observations. Its primary goal is the simultaneous but individual reconstruction of the diffuse and point-like photon flux given a single photon count image, where the fluxes are superimposed. A hierarchical Bayesian parameter model is used to discriminate between morphologically different signal components, yielding a diffuse and a point-like signal estimate for the photon flux components.
dacapo_calibration implements the DaCapo algorithm used in the Planck/LFI 2015 data release for photometric calibration. The code takes as input a set of TODs and calibrates them using the CMB dipole signal. DaCapo is a variant of the well-known family of destriping algorithms for map-making.
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
DALI (Derivative Approximation for LIkelihoods) is a fast approximation of non-Gaussian likelihoods. It extends the Fisher Matrix in a straightforward way and allows for a wider range of posterior shapes. The code is written in C/C++.
DALiuGE provides a distributed data management platform and a scalable pipeline execution environment to support continuous, soft real-time, data-intensive processing for producing radio astronomy data products; it originated from a prototyping activity as part of the SKA SDP Consortium called Data Flow Management System (DFMS). Though the development of DALiuGE is largely based on radio astronomy processing requirements, it has adopted a generic, data-driven framework architecture potentially applicable to many other data-intensive applications.
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.
DaMaSCUS-SUN is a Monte Carlo tool simulating the process of solar reflection of dark matter (DM) particles. It provides precise estimates of the DM particle flux reflected by the Sun and passing through a direct detection experiment on Earth. One application is to compute exclusion limits for low DM masses based on nuclear and electron recoil experiments.
DaMaSCUS calculates the density and velocity distribution of dark matter (DM) at any detector of given depth and latitude to provide dark matter particle trajectories inside the Earth. Provided a strong enough DM-matter interaction, the particles scatter on terrestrial atoms and get decelerated and deflected. The resulting local modifications of the DM velocity distribution and number density can have important consequences for direct detection experiments, especially for light DM, and lead to signatures such as diurnal modulations depending on the experiment's location on Earth. The code involves both the Monte Carlo simulation of particle trajectories and generation of data as well as the data analysis consisting of non-parametric density estimation of the local velocity distribution functions and computation of direct detection event rates.
DAME (DAta Mining & Exploration) is an innovative, general purpose, Web-based, VObs compliant, distributed data mining infrastructure specialized in Massive Data Sets exploration with machine learning methods. Initially fine tuned to deal with astronomical data only, DAME has evolved in a general purpose platform which has found applications also in other domains of human endeavor.
DAMIT (Database of Asteroid Models from Inversion Techniques) is a database of three-dimensional models of asteroids computed using inversion techniques; it provides access to reliable and up-to-date physical models of asteroids, i.e., their shapes, rotation periods, and spin axis directions. Models from DAMIT can be used for further detailed studies of individual objects as well as for statistical studies of the whole set. The source codes for lightcurve inversion routines together with brief manuals, sample lightcurves, and the code for the direct problem are available for download.
The Monte Carlo code DAMOCLES models the effects of dust, composed of any combination of species and grain size distributions, on optical and NIR emission lines emitted from the expanding ejecta of a late-time (> 1 yr) supernova. The emissivity and dust distributions follow smooth radial power-law distributions; any arbitrary distribution can be specified by providing the appropriate grid. DAMOCLES treats a variety of clumping structures as specified by a clumped dust mass fraction, volume filling factor, clump size and clump power-law distribution, and the emissivity distribution may also initially be clumped. The code has a large number of variable parameters ranging from 5 dimensions in the simplest models to > 20 in the most complex cases.
DanIDL provides IDL functions and routines for many standard astronomy needs, such as searching for matching points between two coordinate lists of two-dimensional points where each list corresponds to a different coordinate space, estimating the full-width half-maximum (FWHM) and ellipticity of the PSF of an image, calculating pixel variances for a set of calibrated image data, and fitting a 3-parameter plane model to image data. The library also supplies astrometry, general image processing, and general scientific applications.
The DAOPHOT program exploits the capability of photometrically linear image detectors to perform stellar photometry in crowded fields. Raw CCD images are prepared prior to analysis, and following the obtaining of an initial star list with the FIND program, synthetic aperture photometry is performed on the detected objects with the PHOT routine. A local sky brightness and a magnitude are computed for each star in each of the specified stellar apertures, and for crowded fields, the empirical point-spread function must then be obtained for each data frame. The GROUP routine divides the star list for a given frame into optimum subgroups, and then the NSTAR routine is used to obtain photometry for all the stars in the frame by means of least-squares profile fits.
DAOSPEC is a Fortran code for measuring equivalent widths of absorption lines in stellar spectra with minimal human involvement. It works with standard FITS format files and it is designed for use with high resolution (R>15000) and high signal-to-noise-ratio (S/N>30) spectra that have been binned on a linear wavelength scale. First, we review the analysis procedures that are usually employed in the literature. Next, we discuss the principles underlying DAOSPEC and point out similarities and differences with respect to conventional measurement techniques. Then experiments with artificial and real spectra are discussed to illustrate the capabilities and limitations of DAOSPEC, with special attention given to the issues of continuum placement; radial velocities; and the effects of strong lines and line crowding. Finally, quantitative comparisons with other codes and with results from the literature are also presented.
DARK SAGE is a semi-analytic model of galaxy formation that focuses on detailing the structure and evolution of galaxies' discs. The code-base, written in C, is an extension of SAGE (ascl:1601.006) and maintains the modularity of SAGE. DARK SAGE runs on any N-body simulation with trees organized in a supported format and containing a minimum set of basic halo properties.
DarkBit computes dark matter constraints on extensions to the Standard Model of particle physics. Written in the GAMBIT (ascl:1708.030) framework, it seamlessly integrates with other tools in the statistical fitting framework; it is also available as a standalone tool. It offers a signal yield calculator for gamma-ray observations, provides likelihoods for arbitrary combinations of spin-independent and spin-dependent scattering processes, and provides a general solution for studying complex particle physics models that predict dark matter annihilation to a multitude of final states.
DarkCapPy calculates rates associated with dark matter capture in the Earth, annihilation into light mediators, and observable decay of the light mediators near the surface of the Earth. This Python/Jupyter package can calculate the Sommerfeld enhancement at the center of the Earth and the timescale for capture-annihilation equilibrium, and can be modified for other compact astronomical objects and mediator spins.
The cosmology code DarkEmulator calculates summary statistics of large scale structure constructed as a part of Dark Quest Project. The “dark_emulator” python package enables fast and accurate computations of halo clustering quantities. The code supports the halo mass function, halo-matter cross-correlation, and halo auto-correlation as a function of halo masses, redshift, separations and cosmological models.
DarkHistory calculates the global temperature and ionization history of the universe given an exotic source of energy injection, such as dark matter annihilation or decay. The software simultaneously solves for the evolution of the free electron fraction and gas temperature, and for the cooling of annihilation/decay products and the secondary particles produced in the process. Consequently, we can self-consistently include the effects of both astrophysical and exotic sources of heating and ionization, and automatically take into account backreaction, where modifications to the ionization/temperature history in turn modify the energy-loss processes for injected particles.
DarkSirensStat statistically measures modified gravitational wave (GW) propagation and the Hubble parameter. The package implements a hierarchical Bayesian framework for constraining the Hubble parameter and modified GW propagation with dark sirens and galaxy catalogs. The package downloads the needed data; which include the GLADE galaxy catalog, O2 and O3 skymaps from the LVC official data releases, and O2 and O3 strain sensitivities. The default options are for running inference for H0 on the O3 BBH events, with flat prior between 20 and 140, mask completeness with 9 masks, interpolation between multiplicative and homogeneous completion, B-band luminosity weights, and a completeness threshold of 50%. The selection effects are computed with MC.
DarkSUSY, written in Fortran, is a publicly-available advanced numerical package for neutralino dark matter calculations. In DarkSUSY one can compute the neutralino density in the Universe today using precision methods which include resonances, pair production thresholds and coannihilations. Masses and mixings of supersymmetric particles can be computed within DarkSUSY or with the help of external programs such as FeynHiggs, ISASUGRA and SUSPECT. Accelerator bounds can be checked to identify viable dark matter candidates. DarkSUSY also computes a large variety of astrophysical signals from neutralino dark matter, such as direct detection in low-background counting experiments and indirect detection through antiprotons, antideuterons, gamma-rays and positrons from the Galactic halo or high-energy neutrinos from the center of the Earth or of the Sun.
DarpanX computes reflectivity and other specular optical functions of a multilayer or single layer mirror for different energy and angles as well as to fit the XRR measurements of the mirrors. It can be used as a standalone package. It has also been implemented as a local module for XSPEC (ascl:9910.005), which is accessible through and requires PyXspec (ascl:2101.014), and can accurately fit experimentally measured X-ray reflectivity data. DarpanX is implemented as a Python 3 module and an API is provided to access the underlying algorithms.
Darth Fader is a wavelet-based method for extracting spectral features from very noisy spectra. Spectra for which a reliable redshift cannot be measured are identified and removed from the input data set automatically, resulting in a clean catalogue that gives an extremely low rate of catastrophic failures even when the spectra have a very low S/N. This technique may offer a significant boost in the number of faint galaxies with accurately determined redshifts.
DASH classifies the type, age, redshift and host for any supernova spectra based on the learned features, through use of a deep convolutional neural network to train a matching algorithm, of each supernova’s type and age. The Python library allows a user to classify spectra; the software is fast and can classify thousands of spectra in seconds. A graphical interface that enables a user to view and classify a spectrum is also available.
DASTCOM5 is a portable direct-access database containing all NASA/JPL asteroid and comet orbit solutions, and the software to access it. Available data include orbital elements, orbit diagrams, physical parameters, and discovery circumstances. A JPL implementation of the software is available at http://ssd.jpl.nasa.gov/sbdb.cgi.
DATACUBE is a command-line package for manipulating and visualizing data cubes. It was designed for integral field spectroscopy but has been extended to be a generic data cube tool, used in particular for sub-millimeter data cubes from the James Clerk Maxwell Telescope. It is part of the Starlink software collection (ascl:1110.012).
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.
DBSP_DRP reduces data from the Palomar spectrograph DBSP. Built on top of PypeIt (ascl:1911.004), it automates the reduction, fluxing, telluric correction, and combining of the red and blue sides of one night's data. The pipeline also provides several GUIs for easier control of the reduction, with one for selecting which data to reduce, and verifying the correctness of FITS headers in an editable table. Another GUI manually places traces for a sort of manually "forced" spectroscopy with the -m option, and after manually placing traces, manually selects sky regions and tweaks the FWHM of the manual traces.
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