Results 551-600 of 2182 (2144 ASCL, 38 submitted)
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.).
ehtim (eht-imaging) simulates and manipulates VLBI data and produces images with regularized maximum likelihood methods. The package contains several primary classes for loading, simulating, and manipulating VLBI data. The main classes are the Image, Array, Obsdata, Imager, and Caltable classes, which provide tools for loading images and data, producing simulated data from realistic u-v tracks, calibrating, inspecting, and plotting data, and producing images from data sets in various polarizations using various data terms and regularizers.
EightBitTransit calculates the light curve of any pixelated image transiting a star and inverts a light curve to recover the "shadow image" that produced it.
The Einstein Toolkit is a collection of software components and tools for simulating and analyzing general relativistic astrophysical systems. Such systems include gravitational wave space-times, collisions of compact objects such as black holes or neutron stars, accretion onto compact objects, core collapse supernovae and Gamma-Ray Bursts.
The Einstein Toolkit builds on numerous software efforts in the numerical relativity community including CactusEinstein, Whisky, and Carpet. The Einstein Toolkit currently uses the Cactus Framework as the underlying computational infrastructure that provides large-scale parallelization, general computational components, and a model for collaborative, portable code development.
eleanor extracts target pixel files from TESS Full Frame Images and produces systematics-corrected light curves for any star observed by the TESS mission. eleanor takes a TIC ID, a Gaia source ID, or (RA, Dec) coordinates of a star observed by TESS and returns, as a single object, a light curve and accompanying target pixel data. The process can be customized, allowing, for example, examination of intermediate data products and changing the aperture used for light curve extraction. eleanor also offers tools that make it easier to work with stars observed in multiple TESS sectors.
ellc analyzes the light curves of detached eclipsing binary stars and transiting exoplanet systems. The model represents stars as triaxial ellipsoids, and the apparent flux from the binary is calculated using Gauss-Legendre integration over the ellipses that are the projection of these ellipsoids on the sky. The code can also calculate the fluxweighted radial velocity of the stars during an eclipse (Rossiter-McLaghlin effect). ellc can model a wide range of eclipsing binary stars and extrasolar planetary systems, and can enable the use of modern Monte Carlo methods for data analysis and model testing.
A Monte Carlo program for the simulation of electromagnetic cascades initiated by high-energy photons and electrons interacting with extragalactic background light (EBL) is presented. Pair production and inverse Compton scattering on EBL photons as well as synchrotron losses and deflections of the charged component in extragalactic magnetic fields (EGMF) are included in the simulation. Weighted sampling of the cascade development is applied to reduce the number of secondary particles and to speed up computations. As final result, the simulation procedure provides the energy, the observation angle, and the time delay of secondary cascade particles at the present epoch. Possible applications are the study of TeV blazars and the influence of the EGMF on their spectra or the calculation of the contribution from ultrahigh energy cosmic rays or dark matter to the diffuse extragalactic gamma-ray background. As an illustration, we present results for deflections and time-delays relevant for the derivation of limits on the EGMF.
The star cluster evolution code Evolve Me A Cluster of StarS (EMACSS) is a simple yet physically motivated computational model that describes the evolution of some fundamental properties of star clusters in static tidal fields. The prescription is based upon the flow of energy within the cluster, which is a constant fraction of the total energy per half-mass relaxation time. According to Henon's predictions, this flow is independent of the precise mechanisms for energy production within the core, and therefore does not require a complete description of the many-body interactions therein. Dynamical theory and analytic descriptions of escape mechanisms is used to construct a series of coupled differential equations expressing the time evolution of cluster mass and radius for a cluster of equal-mass stars. These equations are numerically solved using a fourth-order Runge-Kutta integration kernel; the results were benchmarked against a data base of direct N-body simulations. EMACSS is publicly available and reproduces the N-body results to within ~10 per cent accuracy for the entire post-collapse evolution of star clusters.
emcee is an extensible, pure-Python implementation of Goodman & Weare's Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler. It's designed for Bayesian parameter estimation. The algorithm behind emcee has several advantages over traditional MCMC sampling methods and has excellent performance as measured by the autocorrelation time (or function calls per independent sample). One advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to $sim N^2$ for a traditional algorithm in an N-dimensional parameter space. Exploiting the parallelism of the ensemble method, emcee permits any user to take advantage of multiple CPU cores without extra effort.
Emerge (Empirical ModEl for the foRmation of GalaxiEs) populates dark matter halo merger trees with galaxies using simple empirical relations between galaxy and halo properties. For each model represented by a set of parameters, it computes a mock universe, which it then compares to observed statistical data to obtain a likelihood. Parameter space can be explored with several advanced stochastic algorithms such as MCMC to find the models that are in agreement with the observations.
The determination of the EM gain of the CCD is best done by fitting the histogram of many low-light frames. Typically, the dark+CIC noise of a 30ms frame itself is a sufficient amount of signal to determine accurately the EM gain with about 200 512x512 frames. The IDL code emGain takes as an input a cube of frames and fit the histogram of all the pixels with the EM stage output probability function. The function returns the EM gain of the frames as well as the read-out noise and the mean signal level of the frames.
empiriciSN generates realistic supernova parameters given photometric observations of a potential host galaxy, based entirely on empirical correlations measured from supernova datasets. It is intended to be used to improve supernova simulation for DES and LSST. It is extendable such that additional datasets may be added in the future to improve the fitting algorithm or so that additional light curve parameters or supernova types may be fit.
Emu CMB is a fast emulator the CMB temperature power spectrum based on CAMB (Jan 2010 version). Emu CMB is based on a "space-filling" Orthogonal Array Latin Hypercube design in a de-correlated parameter space obtained by using a fiducial WMAP5 CMB Fisher matrix as a rotation matrix. This design strategy allows for accurate interpolation with small numbers of simulation design points. The emulator presented here is calibrated with 100 CAMB runs that are interpolated over the design space using a global quadratic polynomial fit.
We present a method to numerically estimate the densities of a discretely sampled data based on a binary space partitioning tree. We start with a root node containing all the particles and then recursively divide each node into two nodes each containing roughly equal number of particles, until each of the nodes contains only one particle. The volume of such a leaf node provides an estimate of the local density and its shape provides an estimate of the variance. We implement an entropy-based node splitting criterion that results in a significant improvement in the estimation of densities compared to earlier work. The method is completely metric free and can be applied to arbitrary number of dimensions. We use this method to determine the appropriate metric at each point in space and then use kernel-based methods for calculating the density. The kernel-smoothed estimates were found to be more accurate and have lower dispersion. We apply this method to determine the phase-space densities of dark matter haloes obtained from cosmological N-body simulations. We find that contrary to earlier studies, the volume distribution function v(f) of phase-space density f does not have a constant slope but rather a small hump at high phase-space densities. We demonstrate that a model in which a halo is made up by a superposition of Hernquist spheres is not capable in explaining the shape of v(f) versus f relation, whereas a model which takes into account the contribution of the main halo separately roughly reproduces the behaviour as seen in simulations. The use of the presented method is not limited to calculation of phase-space densities, but can be used as a general purpose data-mining tool and due to its speed and accuracy it is ideally suited for analysis of large multidimensional data sets.
Encube is a qualitative, quantitative and comparative visualization and analysis framework, with application to high-resolution, immersive three-dimensional environments and desktop displays, providing a capable visual analytics experience across the display ecology. Encube includes mechanisms for the support of: 1) interactive visual analytics of sufficiently large subsets of data; 2) synchronous and asynchronous collaboration; and 3) documentation of the discovery workflow. The framework is modular, allowing additional functionalities to be included as required.
Enrico analyzes Fermi data. It produces spectra (model fit and flux points), maps and lightcurves for a target by editing a config file and running a python script which executes the Fermi science tool chain.
ENTERPRISE (Enhanced Numerical Toolbox Enabling a Robust PulsaR Inference SuitE) is a pulsar-timing analysis code which performs noise analysis, gravitational-wave searches, and timing model analysis. It uses Tempo2 (ascl:1210.015) to find the maximum-likelihood fit for the timing parameters and the basis of the fit for the red noise parameters if they are significant.
Enzo is an adaptive mesh refinement (AMR), grid-based hybrid code (hydro + N-Body) which is designed to do simulations of cosmological structure formation. It uses the algorithms of Berger & Collela to improve spatial and temporal resolution in regions of large gradients, such as gravitationally collapsing objects. The Enzo simulation software is incredibly flexible, and can be used to simulate a wide range of cosmological situations with the available physics packages.
Enzo has been parallelized using the MPI message-passing library and can run on any shared or distributed memory parallel supercomputer or PC cluster. Simulations using as many as 1024 processors have been successfully carried out on the San Diego Supercomputing Center's Blue Horizon, an IBM SP.
E-field Parallel Imaging Correlator (EPIC), a highly parallelized Object Oriented Python package, implements the Modular Optimal Frequency Fourier (MOFF) imaging technique. It also includes visibility-based imaging using the software holography technique and a simulator for generating electric fields from a sky model. EPIC can accept dual-polarization inputs and produce images of all four instrumental cross-polarizations.
EPICS is a set of software tools and applications developed collaboratively and used to create distributed soft real-time control systems for scientific instruments such as particle accelerators and telescopes. Such distributed control systems typically comprise tens or even hundreds of computers, networked together to allow communication between them and to provide control and feedback of the various parts of the device from a central control room, or even remotely over the internet. EPICS uses Client/Server and Publish/Subscribe techniques to communicate between the various computers. A Channel Access Gateway allows engineers and physicists elsewhere in the building to examine the current state of the IOCs, but prevents them from making unauthorized adjustments to the running system. In many cases the engineers can make a secure internet connection from home to diagnose and fix faults without having to travel to the site.
EPICS is used by many facilities worldwide, including the Advanced Photon Source at Argonne National Laboratory, Fermilab, Keck Observatory, Laboratori Nazionali di Legnaro, Brazilian Synchrotron Light Source, Los Alamos National Laboratory, Australian Synchrotron, and Stanford Linear Accellerator Center.
EPOS (Exoplanet Population Observation Simulator) simulates observations of exoplanet populations. It provides an interface between planet formation simulations and exoplanet surveys such as Kepler. EPOS can also be used to estimate planet occurrence rates and the orbital architectures of planetary systems.
epsnoise simulates pixel noise in weak-lensing ellipticity and shear measurements. This open-source python code can efficiently create an intrinsic ellipticity distribution, shear it, and add noise, thereby mimicking a "perfect" measurement that is not affected by shape-measurement biases. For theoretical studies, we provide the Marsaglia distribution, which describes the ratio of normal variables in the general case of non-zero mean and correlation. We also added a convenience method that evaluates the Marsaglia distribution for the ratio of moments of a Gaussian-shaped brightness distribution, which gives a very good approximation of the measured ellipticity distribution also for galaxies with different radial profiles. We provide four shear estimators, two based on the ε ellipticity measure, two on χ. While three of them are essentially plain averages, we introduce a new estimator which requires a functional minimization.
eqpair computes the electron energy distribution resulting from a balance between heating and direct acceleration of particles, and cooling processes. Electron-positron pair balance, bremstrahlung, and Compton cooling, including external soft photon input, are among the processes considered, and the final electron distribution can be hybrid, thermal, or non-thermal.
The Fortran program EQUIB solves the statistical equilibrium equation for each ion and yields atomic level populations and line emissivities for given physical conditions, namely electron temperature and electron density, appropriate to the zones in an ionized nebula where the ions are expected to exist.
The ESO-MIDAS system provides general tools for image processing and data reduction with emphasis on astronomical applications including imaging and special reduction packages for ESO instrumentation at La Silla and the VLT at Paranal. In addition it contains applications packages for stellar and surface photometry, image sharpening and decomposition, statistics, data fitting, data presentation in graphical form, and more.
EsoRex (ESO Recipe Execution Tool) lists, configures, and executes Common Pipeline Library (CPL) (ascl:1402.010) recipes from the command line. Its features include automatically generating configuration files, recursive recipe-path searching, command line and configuration file parameters, and recipe product naming control, among many others.
ESP (Extended Surface Photometry) determines the photometric properties of galaxies and other extended objects. It has applications that detect flatfielding faults, remove cosmic rays, median filter images, determine image statistics and local background values, perform galaxy profiling, fit 2-D Gaussian profiles to galaxies, generate pie slice cross-sections of galaxies, and display profiling results. It is distributed as part of the Starlink software collection (ascl:1110.012).
The ESTER code computes the steady state of an isolated star of mass larger than two solar masses. The only convective region computed as such is the core where isentropy is assumed. ESTER provides solutions of the partial differential equations, for the pressure, density, temperature, angular velocity and meridional velocity for the whole volume. The angular velocity (differential rotation) and meridional circulation are computed consistently with the structure and are driven by the baroclinic torque. The code uses spectral methods, both radially and horizontally, with spherical harmonics and Chebyshev polynomials. The iterations follow Newton's algorithm. The code is object-oriented and is written in C++; a python suite allows an easy visualization of the results. While running, PGPLOT graphs are displayed to show evolution of the iterations.
Written for the Wide-Field Infrared Survey Telescope (WFIRST) high-latitude survey, the exposure time calculator (ETC) works in both imaging and spectroscopic modes. In addition to the standard ETC functions (e.g. background and S/N determination), the calculator integrates over the galaxy population and forecasts the density and redshift distribution of galaxy shapes usable for weak lensing (in imaging mode) and the detected emission lines (in spectroscopic mode). The program may be useful outside of WFIRST but no warranties are made regarding its suitability for general purposes. The software is available for download; IPAC maintains a web interface for those who wish to run a small number of cases without having to download the package.
ETC++ is a exposure-time calculator that considers the effect of cosmic rays, undersampling, dithering, and imperfect pixel response functions. Errors on astrometry and galaxy shape measurements can be predicted as well as photometric errors.
EVEREST (EPIC Variability Extraction and Removal for Exoplanet Science Targets) removes instrumental noise from light curves with pixel level decorrelation and Gaussian processes. The code, written in Python, generates the EVEREST catalog and offers tools for accessing and interacting with the de-trended light curves. EVEREST exploits correlations across the pixels on the CCD to remove systematics introduced by the spacecraft’s pointing error. For K2, it yields light curves with precision comparable to that of the original Kepler mission. Interaction with the EVEREST catalog catalog is available via the command line and through the Python interface. Though written for K2, EVEREST can be applied to additional surveys, such as the TESS mission, to correct for instrumental systematics and enable the detection of low signal-to-noise transiting exoplanets.
evolstate assigns crude evolutionary states (main-sequence, subgiant, red giant) to stars given an input temperature and radius/surface gravity, based on physically motivated boundaries from solar metallicity interior models.
The EXtraction of COsmological Parameters software (EXCOP) is a set of C and IDL programs together with a very large database of cosmological models generated by CMBFAST that will compute likelihood functions for cosmological parameters given some CMB data. This is the software and database used in the Stompor et al. (2001) analysis of a high resoultion Maxima1 CMB anisotropy map.
The heterogeneity of papers dealing with the discovery and characterization of exoplanets makes every attempt to maintain a uniform exoplanet catalog almost impossible. Four sources currently available online (NASA Exoplanet Archive, Exoplanet Orbit Database, Exoplanet Encyclopaedia, and Open Exoplanet Catalogue) are commonly used by the community, but they can hardly be compared, due to discrepancies in notations and selection criteria.
Exo-MerCat is a Python code that collects and selects the most precise measurement for all interesting planetary and orbital parameters contained in the four databases, accounting for the presence of multiple aliases for the same target. It can download information about the host star as well by the use of Virtual Observatory ConeSearch connections to the major archives such as SIMBAD and those available in VizieR. A Graphical User Interface is provided to filter data based on the user's constraints and generate automatic plots that are commonly used in the exoplanetary community.
With Exo-MerCat, we retrieved a unique catalog that merges information from the four main databases, standardizing the output and handling notation differences issues. Exo-MerCat can correct as many issues that prevent a direct correspondence between multiple items in the four databases as possible, with the available data. The catalog is available as a VO resource for everyone to use and it is periodically updated, according to the update rates of the source catalogs.
EXO-NAILER (EXOplanet traNsits and rAdIal veLocity fittER) efficiently fits exoplanet transit lightcurves, radial velocities (RVs) or both. The code handles data taken with different instruments. For RVs, a different center-of-mass velocity can be fitted for each instrument to account for offsets between them; if jitter is included, a different jitter term can also fitted for each instrument. For transits, a different photometric jitter can be fitted to each instrument as can different limb-darkening coefficients and different transit depths. In addition to general options that need to be set, EXO-NAILER also requires that photometry and radial velocity options be defined for each instrument.
Exo-Transmit calculates the transmission spectrum of an exoplanet atmosphere given specified input information about the planetary and stellar radii, the planet's surface gravity, the atmospheric temperature-pressure (T-P) profile, the location (in terms of pressure) of any cloud layers, the composition of the atmosphere, and opacity data for the atoms and molecules that make up the atmosphere. The code solves the equation of radiative transfer for absorption of starlight passing through the planet's atmosphere as it transits, accounting for the oblique path of light through the planetary atmosphere along an Earth-bound observer's line of sight. The fraction of light absorbed (or blocked) by the planet plus its atmosphere is calculated as a function of wavelength to produce the wavelength-dependent transmission spectrum. Functionality is provided to simulate the presence of atmospheric aerosols in two ways: an optically thick (gray) cloud deck can be generated at a user-specified height in the atmosphere, and the nominal Rayleigh scattering can be increased by a specified factor.
ExoCAM adapts the NCAR Community Earth System Model (CESM) for planetary and exoplanetary applications. The system files, source code, initial conditions files, and namelists provided do not run standalone. ExoCAM is a patch to be used with standard distributions of CESM version 1.2.1 (http://www.cesm.ucar.edu/models/current.html), and is also intended to be run with ExoRT (ascl:2002.019), a correlated-k radiative transfer package.
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.
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.
ExoData is a python interface for accessing and exploring the Open Exoplanet Catalogue. It allows searching of planets (including alternate names) and easy navigation of hierarchy, parses spectral types and fills in missing parameters based on programmable specifications, and provides easy reference of planet parameters such as GJ1214b.ra, GJ1214b.T, and GJ1214b.R. It calculates values such as transit duration, can easily rescale units, and can be used as an input catalog for large scale simulation and analysis of planets.
EXOFAST is a fast, robust suite of routines written in IDL which is designed to fit exoplanetary transits and radial velocity variations simultaneously or separately, and characterize the parameter uncertainties and covariances with a Differential Evolution Markov Chain Monte Carlo method. Our code self-consistently incorporates both data sets to simultaneously derive stellar parameters along with the transit and RV parameters, resulting in consistent, but tighter constraints on an example fit of the discovery data of HAT-P-3b that is well-mixed in under two minutes on a standard desktop computer. EXOFAST has an easy-to-use online interface for several basic features of our transit and radial velocity fitting. A more robust version of EXOFAST, EXOFASTv2 (ascl:1710.003), is also available.
EXOFASTv2 improves upon EXOFAST (ascl:1207.001) for exoplanet modeling. It uses a differential evolution Markov Chain Monte Carlo code to fit an arbitrary number of transits (each with their own error scaling, normalization, TTV, and/or detrending parameters), an arbitrary number of RV sources (each with their own zero point and jitter), and an arbitrary number of planets, changing nothing but command line arguments and configuration files. The global model includes integrated isochrone and SED models to constrain the stellar properties and can accept priors on any fitted or derived quantities (e.g., parallax from Gaia). It is easily extensible to add additional effects or parameters.
ExoFit is a freely available software package for estimating orbital parameters of extra-solar planets. ExoFit can search for either one or two planets and employs a Bayesian Markov Chain Monte Carlo (MCMC) method to fit a Keplerian radial velocity curve onto the radial velocity data.
ExoGAN (Exoplanets Generative Adversarial Network) analyzes exoplanetary atmospheres using an unsupervised deep-learning algorithm that recognizes molecular features, atmospheric trace-gas abundances, and planetary parameters. After training, ExoGAN can be applied to a large number of instruments and planetary types and can be used either as a final atmospheric analysis or to provide prior constraints to subsequent retrieval.
exoinformatics computes the entropy of a planetary system's size ordering using three different entropy methods: tally-scores, integral path, and change points.
ExoPlanet provides a graphical interface for the construction, evaluation and application of a machine learning model in predictive analysis. With the back-end built using the numpy and scikit-learn libraries, ExoPlanet couples fast and well tested algorithms, a UI designed over the PyQt framework, and graphs rendered using Matplotlib. This serves to provide the user with a rich interface, rapid analytics and interactive visuals.
ExoPlanet is designed to have a minimal learning curve to allow researchers to focus more on the applicative aspect of machine learning algorithms rather than their implementation details and supports both methods of learning, providing algorithms for unsupervised and supervised training, which may be done with continuous or discrete labels. The parameters of each algorithms can be adjusted to ensure the best fit for the data. Training data is read from a CSV file, and after training is complete, ExoPlanet automates the building of the visual representations for the trained model. Once training and evaluation yield satisfactory results, the model may be used to make data based predictions on a new data set.
exoplanet is a toolkit for probabilistic modeling of transit and/or radial velocity observations of exoplanets and other astronomical time series using PyMC3 (ascl:1610.016), a flexible and high-performance model building language and inference engine. exoplanet extends PyMC3's language to support many of the custom functions and distributions required when fitting exoplanet datasets. These features include a fast and robust solver for Kepler's equation; scalable Gaussian processes using celerite (ascl:1709.008); and fast and accurate limb darkened light curves using the code starry (ascl:1810.005). It also offers common reparameterizations for limb darkening parameters, and planet radius and impact parameters.
Exoplanet determines the posterior distribution of exoplanets by use of a trans-dimensional Markov Chain Monte Carlo method within Nested Sampling. This method finds the posterior distribution in a single run rather than requiring multiple runs with trial values.
Exopop is a general hierarchical probabilistic framework for making justified inferences about the population of exoplanets. Written in python, it requires that the occurrence rate density be a smooth function of period and radius (employing a Gaussian process) and takes survey completeness and observational uncertainties into account. Exopop produces more accurate estimates of the whole population than standard procedures based on weighting by inverse detection efficiency.
ExoPriors calculates a log-likelihood penalty for an input set of transit parameters to account for observational bias (geometric and signal-to-noise ratio detection bias) of transiting exoplanets. Written in Python, the code calculates this log-likelihood penalty in one of seven user-specified cases specified with Boolean input parameters for geometric and/or SNR bias, grazing or non-grazing events, and occultation events.
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