Results 401-450 of 1927 (1899 ASCL, 28 submitted)
This code provides a method for detecting cosmic rays in single images. The algorithm is based on a simple analysis of the histogram of the image data and does not use any modeling of the picture of the object. It does not require a good signal-to-noise ratio in the image data. Identification of multiple-pixel cosmic-ray hits is realized by running the procedure for detection and replacement iteratively. The method is very effective when applied to the images with spectroscopic data, and is also very fast in comparison with other single-image algorithms found in astronomical data-processing packages. Practical implementation and examples of application are presented in the code paper.
ddisk is an IDL script that calculates the time-evolution of a circumstellar debris disk. It calculates dust abundances over time for a debris-disk that is produced by a planetesimal disk that is grinding away due to collisional erosion.
DDS simulates scattered light and thermal reemission in arbitrary optically dust distributions with spherical, homogeneous grains where the dust parameters (optical properties, sublimation temperature, grain size) and SED of the illuminating/ heating radiative source can be arbitrarily defined. The code is optimized for studying circumstellar debris disks where large grains (i.e., with large size parameters) are expected to determine the far-infrared through millimeter dust reemission spectral energy distribution. The approach to calculate dust temperatures and dust reemission spectra is only valid in the optically thin regime. The validity of this constraint is verified for each model during the runtime of the code. The relative abundances of different grains can be arbitrarily chosen, but must be constant outside the dust sublimation region., i.e., the shape of the (arbitrary) radial dust density distribution outside the dust sublimation region is the same for all grain sizes and chemistries.
DDSCAT is a freely available software package which applies the "discrete dipole approximation" (DDA) to calculate scattering and absorption of electromagnetic waves by targets with arbitrary geometries and complex refractive index. The DDA approximates the target by an array of polarizable points. DDSCAT.5a requires that these polarizable points be located on a cubic lattice. DDSCAT allows accurate calculations of electromagnetic scattering from targets with "size parameters" 2 pi a/lambda < 15 provided the refractive index m is not large compared to unity (|m-1| < 1). The DDSCAT package is written in Fortran and is highly portable. The program supports calculations for a variety of target geometries (e.g., ellipsoids, regular tetrahedra, rectangular solids, finite cylinders, hexagonal prisms, etc.). Target materials may be both inhomogeneous and anisotropic. It is straightforward for the user to import arbitrary target geometries into the code, and relatively straightforward to add new target generation capability to the package. DDSCAT automatically calculates total cross sections for absorption and scattering and selected elements of the Mueller scattering intensity matrix for specified orientation of the target relative to the incident wave, and for specified scattering directions. This User Guide explains how to use DDSCAT to carry out EM scattering calculations. CPU and memory requirements are described.
DEBiL rapidly fits a large number of light curves to a simple model. It is the central component of a pipeline for systematically identifying and analyzing eclipsing binaries within a large dataset of light curves; the results of DEBiL can be used to flag light curves of interest for follow-up analysis.
DECA performs photometric analysis of images of disk and elliptical galaxies having a regular structure. It is written in Python and combines the capabilities of several widely used packages for astronomical data processing such as IRAF, SExtractor, and the GALFIT code to perform two-dimensional decomposition of galaxy images into several photometric components (bulge+disk). DECA can be applied to large samples of galaxies with different orientations with respect to the line of sight (including edge-on galaxies) and requires minimum human intervention.
DecouplingModes calculates the amplitude of the passive modes, which requires solving the Einstein equations on superhorizon scales sourced by the anisotropic stress from the magnetic fields (prior to neutrino decoupling), and the magnetic and neutrino stress (after decoupling). The code is available as a Mathematica notebook.
Dedalus solves differential equations using spectral methods. It implements flexible algorithms to solve initial-value, boundary-value, and eigenvalue problems with broad ranges of custom equations and spectral domains. Its primary features include symbolic equation entry, multidimensional parallelization, implicit-explicit timestepping, and flexible analysis with HDF5. The code is written primarily in Python and features an easy-to-use interface. The numerical algorithm produces highly sparse systems for many equations which are efficiently solved using compiled libraries and MPI.
DeepMoon trains a convolutional neural net using data derived from a global digital elevation map (DEM) and catalog of craters to recognize craters on the Moon. The TensorFlow-based pipeline code is divided into three parts. The first generates a set images of the Moon randomly cropped from the DEM, with corresponding crater positions and radii. The second trains a convnet using this data, and the third validates the convnet's predictions.
The IDL package Defringeflat identifies and removes fringe patterns from images such as spectrograph flat fields. It uses a wavelet transform to calculate the frequency spectrum in a region around each point of a one-dimensional array. The wavelet transform amplitude is reconstructed from (smoothed) parameters obtaining the fringe's wavelet transform, after which an inverse wavelet transform is performed to obtain the computed fringe pattern which is then removed from the flat.
At the end of inflation, dynamical instability can rapidly deposit the energy of homogeneous cold inflaton into excitations of other fields. This process, known as preheating, is rather violent, inhomogeneous and non-linear, and has to be studied numerically. This paper presents a new code for simulating scalar field dynamics in expanding universe written for that purpose. Compared to available alternatives, it significantly improves both the speed and the accuracy of calculations, and is fully instrumented for 3D visualization. We reproduce previously published results on preheating in simple chaotic inflation models, and further investigate non-linear dynamics of the inflaton decay. Surprisingly, we find that the fields do not want to thermalize quite the way one would think. Instead of directly reaching equilibrium, the evolution appears to be stuck in a rather simple but quite inhomogeneous state. In particular, one-point distribution function of total energy density appears to be universal among various two-field preheating models, and is exceedingly well described by a lognormal distribution. It is tempting to attribute this state to scalar field turbulence.
DELightcurveSimulation simulates light curves with any given power spectral density and any probability density function, following the algorithm described in Emmanoulopoulos et al. (2013). The simulated products have exactly the same variability and statistical properties as the observed light curves. The code is a Python implementation of the Mathematica code provided by Emmanoulopoulos et al.
demc2, also abbreviated as DE-MCMC, is a differential evolution Markov Chain parameter estimation library written in R for adaptive MCMC on real parameter spaces.
DES exposure checker renders science-grade images directly to a web browser and allows users to mark problematic features from a set of predefined classes, thus allowing image quality control for the Dark Energy Survey to be crowdsourced through its web application. Users can also generate custom labels to help identify previously unknown problem classes; generated reports are fed back to hardware and software experts to help mitigate and eliminate recognized issues. These problem reports allow rapid correction of artifacts that otherwise may be too subtle or infrequent to be recognized.
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.
DESPOTIC (Derive the Energetics and SPectra of Optically Thick Interstellar Clouds), written in Python, represents optically thick interstellar clouds using a one-zone model and calculates line luminosities, line cooling rates, and in restricted cases line profiles using an escape probability formalism. DESPOTIC calculates clouds' equilibrium gas and dust temperatures and their time-dependent thermal evolution. The code allows rapid and interactive calculation of clouds' characteristic temperatures, identification of their dominant heating and cooling mechanisms, and prediction of their observable spectra across a wide range of interstellar environments.
DexM (Deus ex Machina) efficiently generates density, halo, and ionization fields on very large scales and with a large dynamic range through seminumeric simulation. These properties are essential for reionization studies, especially those involving rare, massive QSOs, since one must be able to statistically capture the ionization field. DexM can also generate ionization fields directly from the evolved density field to account for the ionizing contribution of small halos. Semi-numerical simulations use more approximate physics than numerical simulations, but independently generate 3D cosmological realizations. DexM is portable and fast, and allows for explorations of wide swaths of astrophysical parameter space and an unprecedented dynamic range.
The NASA Astrophysics Data System (ADS) now holds 1.3 million scanned pages, containing numerous plots and figures for which the original data sets are lost or inaccessible. The availability of scans of the figures can significantly ease the regeneration of the data sets. For this purpose, the ADS has developed Dexter, a Java applet that supports the user in this process. Dexter's basic functionality is to let the user manually digitize a plot by marking points and defining the coordinate transformation from the logical to the physical coordinate system. Advanced features include automatic identification of axes, tracing lines and finding points matching a template.
The FITS format (Flexible Image Transport System) is a widely used format to store astronomical data. It is used to store a lot of different types of data such as 1D or 2D spectra, 3D data cubes. It has been developed in the late 1970 to reach its final form almost two decades ago. FITS files are built with two components. The data themselves are stored as tables and contains any types of data. A header is built containing set of keywords-value pairs aiming at describing the data themselves.
Accessing and displaying metadata inside FITS files is important in order to get an overview of their content properties without having to read the data themselves. It is particularly useful when dealing with large amount of files at once. Tools have been already publicly available for years with the dfits and fitsort algorithms (the documentation is available here https://www.eso.org/sci/software/eclipse/eug/eug/node8.html). The main limitation is that they are stand-alone programs useable only in a terminal. They can not be used natively inside another program.
The python module presented here, dfitspy, is a project that migrates the main dfits and fitsort capabilities to python. It is a metadata searcher/displayer for FITS files. As dfits and fitsort, dfitspy is able to display in the terminal the result of a metadata search and is able to grep certain values of keywords inside large samples of files. Therefore it can be used directly with the command line interface. Nevertheless, dfitspy can be, and it is its strength, imported as a python module and the user can use these functionnalities inside another python code or the python interpretor.
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.
DIAMONDS (high-DImensional And multi-MOdal NesteD Sampling) provides Bayesian parameter estimation and model comparison by means of the nested sampling Monte Carlo (NSMC) algorithm, an efficient and powerful method very suitable for high-dimensional and multi-modal problems; it can be used for any application involving Bayesian parameter estimation and/or model selection in general. Developed in C++11, DIAMONDS is structured in classes for flexibility and configurability. Any new model, likelihood and prior PDFs can be defined and implemented upon a basic template.
DICE models initial conditions of idealized galaxies to study their secular evolution or their more complex interactions such as mergers or compact groups using N-Body/hydro codes. The code can set up a large number of components modeling distinct parts of the galaxy, and creates 3D distributions of particles using a N-try MCMC algorithm which does not require a prior knowledge of the distribution function. The gravitational potential is then computed on a multi-level Cartesian mesh by solving the Poisson equation in the Fourier space. Finally, the dynamical equilibrium of each component is computed by integrating the Jeans equations for each particles. Several galaxies can be generated in a row and be placed on Keplerian orbits to model interactions. DICE writes the initial conditions in the Gadget1 or Gadget2 (ascl:0003.001) format and is fully compatible with Ramses (ascl:1011.007).
DICE is a C++ template library designed to solve collisionless fluid dynamics in 6D phase space using massively parallel supercomputers via an hybrid OpenMP/MPI parallelization. ColDICE, based on DICE, implements a cosmological and physical VLASOV-POISSON solver for cold systems such as dark matter (CDM) dynamics.
The Difference-smoothing MATLAB code measures the time delay from the light curves of images of a gravitationally lendsed quasar. It uses a smoothing timescale free parameter, generates more realistic synthetic light curves to estimate the time delay uncertainty, and uses X2 plot to assess the reliability of a time delay measurement as well as to identify instances of catastrophic failure of the time delay estimator. A systematic bias in the measurement of time delays for some light curves can be eliminated by applying a correction to each measured time delay.
DiffuseModel calculates the scattered radiation from dust scattering in the Milky Way based on stars from the Hipparcos catalog. It uses Monte Carlo to implement multiple scattering and assumes a user-supplied grid for the dust distribution. The output is a FITS file with the diffuse light over the Galaxy. It is intended for use in the UV (900 - 3000 A) but may be modified for use in other wavelengths and galaxies.
Diffusion.f is an exportable subroutine to calculate the diffusion of elements in stars. The routine solves exactly the Burgers equations and can include any number of elements as variables. The code has been used successfully by a number of different groups; applications include diffusion in the sun and diffusion in globular cluster stars. There are many other possible applications to main sequence and to evolved stars. The associated README file explains how to use the subroutine.
Difmap is a program developed for synthesis imaging of visibility data from interferometer arrays of radio telescopes world-wide. Its prime advantages over traditional packages are its emphasis on interactive processing, speed, and the use of Difference mapping techniques.
Software correlation, where a correlation algorithm written in a high-level language such as C++ is run on commodity computer hardware, has become increasingly attractive for small to medium sized and/or bandwidth constrained radio interferometers. In particular, many long baseline arrays (which typically have fewer than 20 elements and are restricted in observing bandwidth by costly recording hardware and media) have utilized software correlators for rapid, cost-effective correlator upgrades to allow compatibility with new, wider bandwidth recording systems and improve correlator flexibility. The DiFX correlator, made publicly available in 2007, has been a popular choice in such upgrades and is now used for production correlation by a number of observatories and research groups worldwide. Here we describe the evolution in the capabilities of the DiFX correlator over the past three years, including a number of new capabilities, substantial performance improvements, and a large amount of supporting infrastructure to ease use of the code. New capabilities include the ability to correlate a large number of phase centers in a single correlation pass, the extraction of phase calibration tones, correlation of disparate but overlapping sub-bands, the production of rapidly sampled filterbank and kurtosis data at minimal cost, and many more. The latest version of the code is at least 15% faster than the original, and in certain situations many times this value. Finally, we also present detailed test results validating the correctness of the new code.
DimReduce is a C++ package for performing nonlinear dimensionality reduction of very large datasets with Locally Linear Embedding (LLE) and its variants. DimReduce is built for speed, using the optimized linear algebra packages BLAS, LAPACK, and ARPACK. Because of the need for storing very large matrices (1000 by 10000, for our SDSS LLE work), DimReduce is designed to use binary FITS files as inputs and outputs. This means that using the code is a bit more cumbersome. For smaller-scale LLE, where speed of computation is not as much of an issue, the Modular Data Processing toolkit may be a better choice. It is a python toolkit with some LLE functionality, which VanderPlas contributed.
DIPSO plots spectroscopic data rapidly and combines analysis and high-quality graphical output in a simple command-line driven interactive environment. It can be used, for example, to fit emission lines, measure equivalent widths and fluxes, do Fourier analysis, and fit models to spectra. A macro facility allows convenient execution of regularly used sequences of commands, and a simple Fortran interface permits "personal" software to be integrated with the program. DIPSO is part of the Starlink software collection (ascl:1110.012).
The Mathematica code DirectDM takes the Wilson coefficients of relativistic operators that couple DM to the SM quarks, leptons, and gauge bosons and matches them onto a non-relativistic Galilean invariant EFT in order to calculate the direct detection scattering rates. A Python implementation of DirectDM is also available (ascl:1806.016).
DirectDM, written in Python, takes the Wilson coefficients of relativistic operators that couple DM to the SM quarks, leptons, and gauge bosons and matches them onto a non-relativistic Galilean invariant EFT in order to calculate the direct detection scattering rates. A Mathematica implementation of DirectDM is also available (ascl:1806.015).
DIRT is a Java applet for modelling astrophysical processes in circumstellar dust shells around young and evolved stars. With DIRT, you can select and display over 500,000 pre-run model spectral energy distributions (SEDs), find the best-fit model to your data set, and account for beam size in model fitting. DIRT also allows you to manipulate data and models with an interactive viewer, display gas and dust density and temperature profiles, and display model intensity profiles at various wavelengths.
Disc2vel derives tangential and radial velocity components in the equatorial plane of a barred stellar disc from the observed line-of-sight velocity, assuming geometry of a thin disc. The code is written in IDL, and the method assumes that the bar is close to steady state (i.e. does not evolve fast) and that both morphology and kinematics are symmetrical with respect to the major axis of the bar.
DISCO evolves orbital fluid motion in two and three dimensions, especially at high Mach number, for studying astrophysical disks. The software uses a moving-mesh approach with a dynamic cylindrical mesh that can shear azimuthally to follow the orbital motion of the gas, thus removing diffusive advection errors and permitting longer timesteps than a static grid. DISCO uses an HLLD Riemann solver and a constrained transport scheme compatible with the mesh motion to implement magnetohydrodynamics.
DiskFit implements procedures for fitting non-axisymmetries in either kinematic or photometric data. DiskFit can analyze H-alpha and CO velocity field data as well as HI kinematics to search for non-circular motions in the disk galaxies. DiskFit can also be used to constrain photometric models of the disc, bar and bulge. It deprecates an earlier version, by a subset of these authors, called velfit.
DiskJockey derives dynamical masses for T Tauri stars using the Keplerian motion of their circumstellar disks, applied to radio interferometric data from the Atacama Large Millimeter Array (ALMA) and the Submillimeter Array (SMA). The package relies on RADMC-3D (ascl:1202.015) to perform the radiative transfer of the disk model. DiskJockey is designed to work in a parallel environment where the calculations for each frequency channel can be distributed to independent processors. Due to the computationally expensive nature of the radiative synthesis, fitting sizable datasets (e.g., SMA and ALMA) will require a substantial amount of CPU cores to explore a posterior distribution in a reasonable timeframe.
DiskSim is a source-code distribution of the SPH accretion disk modeling code previously released in a Windows executable form as FITDisk (ascl:1305.011). The code released now is the full research code in Fortran and can be modified as needed by the user.
DISKSTRUCT is a simple 1+1-D code for modeling protoplanetary disks. It is not based on multidimensional radiative transfer! Instead, a flaring-angle recipe is used to compute the irradiation of the disk, while the disk vertical structure at each cylindrical radius is computed in a 1-D fashion; the models computed with this code are therefore approximate. Moreover, this model cannot deal with the dust inner rim.
In spite of these simplifications and drawbacks, the code can still be very useful for disk studies, for the following reasons:
DISORT (DIScrete Ordinate Radiative Transfer) solves the problem of 1D scalar radiative transfer in a single optical medium, such as a planetary atmosphere. The code correctly accounts for multiple scattering by an isotropic or plane-parallel beam source, internal Planck sources, and reflection from a lower boundary. Provided that polarization effects can be neglected, DISORT efficiently calculates accurate fluxes and intensities at any user-specified angle and location within the user-specified medium.
DisPerSE is open source software for the identification of persistent topological features such as peaks, voids, walls and in particular filamentary structures within noisy sampled distributions in 2D, 3D. Using DisPerSE, structure identification can be achieved through the computation of the discrete Morse-Smale complex. The software can deal directly with noisy datasets via the concept of persistence (a measure of the robustness of topological features). Although developed for the study of the properties of filamentary structures in the cosmic web of galaxy distribution over large scales in the Universe, the present version is quite versatile and should be useful for any application where a robust structure identification is required, such as for segmentation or for studying the topology of sampled functions (for example, computing persistent Betti numbers). Currently, it can be applied can work indifferently on many kinds of cell complex (such as structured and unstructured grids, 2D manifolds embedded within a 3D space, discrete point samples using delaunay tesselation, and Healpix tesselations of the sphere). The only constraint is that the distribution must be defined over a manifold, possibly with boundaries.
distlink computes the minimum orbital intersection distance (MOID), or global minimum of the distance between the points lying on two Keplerian ellipses by finding all stationary points of the distance function, based on solving an algebraic polynomial equation of 16th degree. The program tracks numerical errors and carefully treats nearly degenerate cases, including practical cases with almost circular and almost coplanar orbits. Benchmarks confirm its high numeric reliability and accuracy, and even with its error-controlling overheads, this algorithm is a fast MOID computation method that may be useful in processing large catalogs. Written in C++, the library also includes auxiliary functions.
DMATIS (Dark Matter ATtenuation Importance Sampling) calculates the trajectories of DM particles that propagate in the Earth's crust and the lead shield to reach the DAMIC detector using an importance sampling Monte-Carlo simulation. A detailed Monte-Carlo simulation avoids the deficiencies of the SGED/KS method that uses a mean energy loss description to calculate the lower bound on the DM-proton cross section. The code implementing the importance sampling technique makes the brute-force Monte-Carlo simulation of moderately strongly interacting DM with nucleons computationally feasible. DMATIS is written in Python 3 and MATHEMATICA.
The dmdd package enables simple simulation and Bayesian posterior analysis of recoil-event data from dark-matter direct-detection experiments under a wide variety of scattering theories. It enables calculation of the nuclear-recoil rates for a wide range of non-relativistic and relativistic scattering operators, including non-standard momentum-, velocity-, and spin-dependent rates. It also accounts for the correct nuclear response functions for each scattering operator and takes into account the natural abundances of isotopes for a variety of experimental target elements.
This code is a general Monte Carlo method based on Nested Sampling (NS) for sampling complex probability distributions and estimating the normalising constant. The method uses one or more particles, which explore a mixture of nested probability distributions, each successive distribution occupying ~e^-1 times the enclosed prior mass of the previous distribution. While NS technically requires independent generation of particles, Markov Chain Monte Carlo (MCMC) exploration fits naturally into this technique. This method can achieve four times the accuracy of classic MCMC-based Nested Sampling, for the same computational effort; equivalent to a factor of 16 speedup. An additional benefit is that more samples and a more accurate evidence value can be obtained simply by continuing the run for longer, as in standard MCMC.
DNest3 is a C++ implementation of Diffusive Nested Sampling (ascl:1010.029), a Markov Chain Monte Carlo (MCMC) algorithm for Bayesian Inference and Statistical Mechanics. Relative to older DNest versions, DNest3 has improved performance (in terms of the sampling overhead, likelihood evaluations still dominate in general) and is cleaner code: implementing new models should be easier than it was before. In addition, DNest3 is multi-threaded, so one can run multiple MCMC walkers at the same time, and the results will be combined together.
DOLPHOT is a stellar photometry package that was adapted from HSTphot for general use. It supports two modes; the first is a generic PSF-fitting package, which uses analytic PSF models and can be used for any camera. The second mode uses ACS PSFs and calibrations, and is effectively an ACS adaptation of HSTphot. A number of utility programs are also included with the DOLPHOT distribution, including basic image reduction routines.
The DAOSPEC Output Optimizer pipeline (DOOp) runs efficient and convenient equivalent widths measurements in batches of hundreds of spectra. It uses a series of BASH scripts to work as a wrapper for the FORTRAN code DAOSPEC (ascl:1011.002) and uses IRAF (ascl:9911.002) to automatically fix some of the parameters that are usually set by hand when using DAOSPEC. This allows batch-processing of quantities of spectra that would be impossible to deal with by hand. DOOp was originally built for the large quantity of UVES and GIRAFFE spectra produced by the Gaia-ESO Survey, but just like DAOSPEC, it can be used on any high resolution and high signal-to-noise ratio spectrum binned on a linear wavelength scale.
The parameters of the mutual orbit of eclipsing binaries that are physically connected can be obtained by precision timing of minima over time through light travel time effect, apsidal motion or orbital precession. This, however, requires joint analysis of data from different sources obtained through various techniques and with insufficiently quantified uncertainties. In particular, photometric uncertainties are often underestimated, which yields too small uncertainties in minima timings if determined through analysis of a χ2 surface. The task is even more difficult for double eclipsing binaries, especially those with periods close to a resonance such as CzeV344, where minima get often blended with each other.
This code solves the double binary parameters simultaneously and then uses these parameters to determine minima timings (or more specifically O-C values) for individual datasets. In both cases, the uncertainties (or more precisely confidence intervals) are determined through bootstrap resampling of the original data. This procedure to a large extent alleviates the common problem with underestimated photometric uncertainties and provides a check on possible degeneracies in the parameters and the stability of the results. While there are shortcomings to this method as well when compared to Markov Chain Monte Carlo methods, the ease of the implementation of bootstrapping is a significant advantage.
DPI is a FORTRAN77 library that supplies the symplectic mapping method for binary star systems for the Mercury N-Body software package (ascl:1201.008). The binary symplectic mapping is implemented as a hybrid symplectic method that allows close encounters and collisions between massive bodies and is therefore suitable for planetary accretion simulations.
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