Results 201-300 of 2551 (2505 ASCL, 46 submitted)

[ascl:1403.013]
BAOlab: Image processing program

BAOlab is an image processing package written in C that should run on nearly any UNIX system with just the standard C libraries. It reads and writes images in standard FITS format; 16- and 32-bit integer as well as 32-bit floating-point formats are supported. Multi-extension FITS files are currently not supported. Among its tools are ishape for size measurements of compact sources, mksynth for generating synthetic images consisting of a background signal including Poisson noise and a number of pointlike sources, imconvol for convolving two images (a “source” and a “kernel”) with each other using fast fourier transforms (FFTs) and storing the output as a new image, and kfit2d for fitting a two-dimensional King model to an image.

[ascl:1810.002]
Barcode: Bayesian reconstruction of cosmic density fields

Barcode (BAyesian Reconstruction of COsmic DEnsity fields) samples the primordial density fields compatible with a set of dark matter density tracers after cosmic evolution observed in redshift space. It uses a redshift space model based on the analytic solution of coherent flows within a Hamiltonian Monte Carlo posterior sampling of the primordial density field; this method is applicable to analytically derivable structure formation models, such as the Zel'dovich approximation, but also higher order schemes such as augmented Lagrangian perturbation theory or even particle mesh models. The algorithm is well-suited for analysis of the dark matter cosmic web implied by the observed spatial distribution of galaxy clusters, such as obtained from X-ray, SZ or weak lensing surveys, as well as that of the intergalactic medium sampled by the Lyman alpha forest. In these cases, virialized motions are negligible and the tracers cannot be modeled as point-like objects. Barcode can be used in all of these contexts as a baryon acoustic oscillation reconstruction algorithm.

[ascl:2008.008]
Barry: Modular BAO fitting code

Barry compares different BAO models. It removes as many barriers and complications to BAO model fitting as possible and allows each component of the process to remain independent, allowing for detailed comparisons of individual parts. It contains datasets, model fitting tools, and model implementations incorporating different descriptions of non-linear physics and algorithms for isolating the BAO (Baryon Acoustic Oscillation) feature.

[ascl:1608.004]
BART: Bayesian Atmospheric Radiative Transfer fitting code

Cubillos, Patricio; Blecic, Jasmina; Harrington, Joseph; Rojo, Patricio; Lust, Nate; Bowman, Oliver; Stemm, Madison; Foster, Andrew; Loredo, Thomas J.; Fortney, Jonathan; Madhusudhan, Nikku

BART implements a Bayesian, Monte Carlo-driven, radiative-transfer scheme for extracting parameters from spectra of planetary atmospheres. BART combines a thermochemical-equilibrium code, a one-dimensional line-by-line radiative-transfer code, and the Multi-core Markov-chain Monte Carlo statistical module to constrain the atmospheric temperature and chemical-abundance profiles of exoplanets.

[ascl:1807.018]
BARYCORR: Python interface for barycentric RV correction

BARYCORR is a Python interface for ZBARYCORR (ascl:1807.017); it requires the measured redshift and returns the corrected barycentric velocity and time correction.

[ascl:1808.001]
Barycorrpy: Barycentric velocity calculation and leap second management

barycorrpy (BCPy) is a Python implementation of Wright and Eastman's 2014 code (ascl:1807.017) that calculates precise barycentric corrections well below the 1 cm/s level. This level of precision is required in the search for 1 Earth mass planets in the Habitable Zones of Sun-like stars by the Radial Velocity (RV) method, where the maximum semi-amplitude is about 9 cm/s. BCPy was developed for the pipeline for the next generation Doppler Spectrometers - Habitable-zone Planet Finder (HPF) and NEID. An automated leap second management routine improves upon the one available in Astropy. It checks for and downloads a new leap second file before converting from the UT time scale to TDB. The code also includes a converter for JDUTC to BJDTDB.

[ascl:1601.017]
BASCS: Bayesian Separation of Close Sources

BASCS models spatial and spectral information from overlapping sources and the background, and jointly estimates all individual source parameters. The use of spectral information improves the detection of both faint and closely overlapping sources and increases the accuracy with which source parameters are inferred.

[ascl:1608.007]
BASE-9: Bayesian Analysis for Stellar Evolution with nine variables

Robinson, Elliot; von Hippel, Ted; Stein, Nathan; Stenning, David; Wagner-Kaiser, Rachel; Si, Shijing; van Dyk, David

The BASE-9 (Bayesian Analysis for Stellar Evolution with nine variables) software suite recovers star cluster and stellar parameters from photometry and is useful for analyzing single-age, single-metallicity star clusters, binaries, or single stars, and for simulating such systems. BASE-9 uses a Markov chain Monte Carlo (MCMC) technique along with brute force numerical integration to estimate the posterior probability distribution for the age, metallicity, helium abundance, distance modulus, line-of-sight absorption, and parameters of the initial-final mass relation (IFMR) for a cluster, and for the primary mass, secondary mass (if a binary), and cluster probability for every potential cluster member. The MCMC technique is used for the cluster quantities (the first six items listed above) and numerical integration is used for the stellar quantities (the last three items in the above list).

[ascl:1208.010]
BASE: Bayesian Astrometric and Spectroscopic Exoplanet Detection and Characterization Tool

BASE is a novel program for the combined or separate Bayesian analysis of astrometric and radial-velocity measurements of potential exoplanet hosts and binary stars. The tool fulfills two major tasks of exoplanet science, namely the detection of exoplanets and the characterization of their orbits. BASE was developed to provide the possibility of an integrated Bayesian analysis of stellar astrometric and Doppler-spectroscopic measurements with respect to their binary or planetary companions’ signals, correctly treating the astrometric measurement uncertainties and allowing to explore the whole parameter space without the need for informative prior constraints. The tool automatically diagnoses convergence of its Markov chain Monte Carlo (MCMC[2]) sampler to the posterior and regularly outputs status information. For orbit characterization, BASE delivers important results such as the probability densities and correlations of model parameters and derived quantities. BASE is a highly configurable command-line tool developed in Fortran 2008 and compiled with GFortran. Options can be used to control the program’s behaviour and supply information such as the stellar mass or prior information. Any option can be supplied in a configuration file and/or on the command line.

[ascl:1308.006]
BASIN: Beowulf Analysis Symbolic INterface

BASIN (Beowulf Analysis Symbolic INterface) is a flexible, integrated suite of tools for multiuser parallel data analysis and visualization that allows researchers to harness the power of Beowulf PC clusters and multi-processor machines without necessarily being experts in parallel programming. It also includes general tools for data distribution and parallel operations on distributed data for developing libraries for specific tasks.

[ascl:1510.002]
batman: BAsic Transit Model cAlculatioN in Python

batman provides fast calculation of exoplanet transit light curves and supports calculation of light curves for any radially symmetric stellar limb darkening law. It uses an integration algorithm for models that cannot be quickly calculated analytically, and in typical use, the batman Python package can calculate a million model light curves in well under ten minutes for any limb darkening profile.

[ascl:1612.021]
BaTMAn: Bayesian Technique for Multi-image Analysis

Casado, J.; Ascasibar, Y.; García-Benito, R.; Guidi, G.; Choudhury, O. S.; Bellocchi, E.; Sánchez, S. F.; Díaz, A. I.

Bayesian Technique for Multi-image Analysis (BaTMAn) characterizes any astronomical dataset containing spatial information and performs a tessellation based on the measurements and errors provided as input. The algorithm iteratively merges spatial elements as long as they are statistically consistent with carrying the same information (i.e. identical signal within the errors). The output segmentations successfully adapt to the underlying spatial structure, regardless of its morphology and/or the statistical properties of the noise. BaTMAn identifies (and keeps) all the statistically-significant information contained in the input multi-image (e.g. an IFS datacube). The main aim of the algorithm is to characterize spatially-resolved data prior to their analysis.

[ascl:2101.002]
BAYES-LOSVD: Bayesian framework for non-parametric extraction of the LOSVD

BAYES-LOSVD performs non-parametric extraction of the Line-Of-Sight Velocity Distributions in galaxies. Written in Python, it uses Stan (ascl:1801.003) to perform all the computations and provides reliable uncertainties for all the parameters of the model chosen for the fit. The code comes with a large number of features, including read-in routines for some of the most popular IFU spectrographs and surveys, such as ATLAS3D, CALIFA, MaNGA, MUSE-WFM, SAMI, and SAURON.

[ascl:1505.027]
BAYES-X: Bayesian inference tool for the analysis of X-ray observations of galaxy clusters

The great majority of X-ray measurements of cluster masses in the literature assume parametrized functional forms for the radial distribution of two independent cluster thermodynamic properties, such as electron density and temperature, to model the X-ray surface brightness. These radial profiles (e.g. β-model) have an amplitude normalization parameter and two or more shape parameters. BAYES-X uses a cluster model to parametrize the radial X-ray surface brightness profile and explore the constraints on both model parameters and physical parameters. Bayes-X is programmed in Fortran and uses MultiNest (ascl:1109.006) as the Bayesian inference engine.

[ascl:2002.018]
Bayesfit: Command-line program for combining Tempo2 and MultiNest components

Bayesfit pulls together Tempo2 (ascl:1210.015) and MultiNest (ascl:1109.006) components to provide additional functionality such as the specification of priors; Nelder–Mead optimization of the maximum-posterior point; and the capability of computing the partially marginalized likelihood for a given subset of timing-model parameters. Bayesfit is a single python command-line application.

[ascl:1407.015]
BayesFlare: Bayesian method for detecting stellar flares

BayesFlare identifies flaring events in light curves released by the Kepler mission; it identifies even weak events by making use of the flare signal shape. The package contains functions to perform Bayesian hypothesis testing comparing the probability of light curves containing flares to that of them containing noise (or non-flare-like) artifacts. BayesFlare includes functions in its amplitude-marginalizer suite to account for underlying sinusoidal variations in light curve data; it includes such variations in the signal model, and then analytically marginalizes over them.

[ascl:1209.001]
Bayesian Blocks: Detecting and characterizing local variability in time series

Bayesian Blocks is a time-domain algorithm for detecting localized structures (bursts), revealing pulse shapes within bursts, and generally characterizing intensity variations. The input is raw time series data, in almost any form. Three data modes are elaborated: (1) time-tagged events, (2) binned counts, and (3) measurements at arbitrary times with normal errors. The output is the most probable segmentation of the observation interval into sub-intervals during which the signal is perceptibly constant, i.e. has no statistically significant variations. The idea is not that the source is deemed to actually have this discontinuous, piecewise constant form, rather that such an approximate and generic model is often useful. Treatment of data gaps, variable exposure, extension to piecewise linear and piecewise exponential representations, multi-variate time series data, analysis of variance, data on the circle, other data modes, and dispersed data are included.

This implementation is exact and replaces the greedy, approximate, and outdated algorithm implemented in BLOCK.

[ascl:1711.004]
BayesVP: Full Bayesian Voigt profile fitting

BayesVP offers a Bayesian approach for modeling Voigt profiles in absorption spectroscopy. The code fits the absorption line profiles within specified wavelength ranges and generates posterior distributions for the column density, Doppler parameter, and redshifts of the corresponding absorbers. The code uses publicly available efficient parallel sampling packages to sample posterior and thus can be run on parallel platforms. BayesVP supports simultaneous fitting for multiple absorption components in high-dimensional parameter space. The package includes additional utilities such as explicit specification of priors of model parameters, continuum model, Bayesian model comparison criteria, and posterior sampling convergence check.

[ascl:1805.022]
BCcodes: Bolometric Corrections and Synthetic Stellar Photometry

BCcodes computes bolometric corrections and synthetic colors in up to 5 filters for input values of the stellar parameters Teff, log(g), [Fe/H], E(B-V) and [alpha/Fe].

[ascl:1907.011]
beamconv: Cosmic microwave background detector data simulator

beamconv simulates the scanning of the CMB sky while incorporating realistic beams and scan strategies. It uses (spin-)spherical harmonic representations of the (polarized) beam response and sky to generate simulated CMB detector signal timelines. Beams can be arbitrarily shaped. Pointing timelines can be read in or calculated on the fly; optionally, the results can be binned on the sphere.

[ascl:1905.006]
beamModelTester: Model evaluation for fixed antenna phased array radio telescopes

beamModelTester enables evaluation of models of the variation in sensitivity and apparent polarization of fixed antenna phased array radio telescopes. The sensitivity of such instruments varies with respect to the orientation of the source to the antenna, resulting in variation in sensitivity over altitude and azimuth that is not consistent with respect to frequency due to other geometric effects. In addition, the different relative orientation of orthogonal pairs of linear antennae produces a difference in sensitivity between the antennae, leading to an artificial apparent polarization. Comparing the model with observations made using the given telescope makes it possible evaluate the model's performance; the results of this evaluation can provide a figure of merit for the model and guide improvements to it. This system also enables plotting of results from a single station observation on a variety of parameters.

[ascl:1104.013]
BEARCLAW: Boundary Embedded Adaptive Refinement Conservation LAW package

The BEARCLAW package is a multidimensional, Eulerian AMR-capable computational code written in Fortran to solve hyperbolic systems for astrophysical applications. It is part of AstroBEAR, a hydrodynamic & magnetohydrodynamic code environment designed for a variety of astrophysical applications which allows simulations in 2, 2.5 (i.e., cylindrical), and 3 dimensions, in either cartesian or curvilinear coordinates.

[ascl:1908.013]
BEAST: Bayesian Extinction And Stellar Tool

Gordon, Karl D.; Fouesneau, Morgan; Arab, Heddy; Tchernyshyov, Kirill; Weisz, Daniel R.; Dalcanton, Julianne J.; Williams, Benjamin F.; Bell, Eric F.; Bianchi, Luciana; Boyer, Martha; Choi, Yumi; Dolphin, Andrew; Girardi, Léo; Hogg, David W.; Kalirai, Jason S.; Kapala, Maria; Lewis, Alexia R.; Rix, Hans-Walter; Sandstrom, Karin; Skillman, Evan D.

BEAST (Bayesian Extinction and Stellar Tool) fits the ultraviolet to near-infrared photometric SEDs of stars to extract stellar and dust extinction parameters. The stellar parameters are age (t), mass (M), metallicity (M), and distance (d). The dust extinction parameters are dust column (Av), average grain size (Rv), and mixing between type A and B extinction curves (fA).

[ascl:1306.006]
BEHR: Bayesian Estimation of Hardness Ratios

BEHR is a standalone command-line C program designed to quickly estimate the hardness ratios and their uncertainties for astrophysical sources. It is especially useful in the Poisson regime of low counts, and computes the proper uncertainty regardless of whether the source is detected in both passbands or not.

[submitted]
BELLAMY: A cross-matching package for the cynical astronomer

BELLAMY is a cross-matching algorithm designed primarily for radio images, that aims to match all sources in the supplied target catalogue to sources in a reference catalogue by calculating the probability of a match. BELLAMY utilises not only the position of a source on the sky, but also the flux data to calculate this probability, determining the most probable match in the reference catalog to the target source. Additionally, BELLAMY attempts to undo any spatial distortion that may be affecting the target catalogue, by creating a model of the offsets of matched sources which is then applied to unmatched sources. This combines to produce an iterative cross-matching algorithm that provides the user with an obvious measure of how confident they should be with the results of a cross-match.

[ascl:1306.013]
Bessel: Fast Bessel Function Jn(z) Routine for Large n,z

Bessel, written in the C programming language, uses an accurate scheme for evaluating Bessel functions of high order. It has been extensively tested against a number of other routines, demonstrating its accuracy and efficiency.

[ascl:1901.009]
bettermoments: Line-of-sight velocity calculation

bettermoments measures precise line-of-sight velocities from Doppler shifted lines to determine small scale deviations indicative of, for example, embedded planets.

[ascl:1402.015]
BF_dist: Busy Function fitting

Westmeier, Tobias; Jurek, Russell; Obreschkow, Danail; Koribalski, Bärbel S.; Staveley-Smith, Lister

The "busy function" accurately describes the characteristic double-horn HI profile of many galaxies. Implemented in a C/C++ library and Python module called BF_dist, it is a continuous, differentiable function that consists of only two basic functions, the error function, erf(x), and a polynomial, |x|^n, of degree n >= 2. BF_dist offers great flexibility in fitting a wide range of HI profiles from the Gaussian profiles of dwarf galaxies to the broad, asymmetric double-horn profiles of spiral galaxies, and can be used to parametrize observed HI spectra of galaxies and the construction of spectral templates for simulations and matched filtering algorithms accurately and efficiently.

[ascl:1504.020]
BGLS: A Bayesian formalism for the generalised Lomb-Scargle periodogram

BGLS calculates the Bayesian Generalized Lomb-Scargle periodogram. It takes as input arrays with a time series, a dataset and errors on those data, and returns arrays with sampled periods and the periodogram values at those periods.

[ascl:1806.002]
BHDD: Primordial black hole binaries code

BHDD (BlackHolesDarkDress) simulates primordial black hole (PBH) binaries that are clothed in dark matter (DM) halos. The software uses N-body simulations and analytical estimates to follow the evolution of PBH binaries formed in the early Universe.

[ascl:1206.005]
bhint: High-precision integrator for stellar systems

bhint is a post-Newtonian, high-precision integrator for stellar systems surrounding a super-massive black hole. The algorithm makes use of the fact that the Keplerian orbits in such a potential can be calculated directly and are only weakly perturbed. For a given average number of steps per orbit, bhint is almost a factor of 100 more accurate than the standard Hermite method.

[ascl:1802.013]
BHMcalc: Binary Habitability Mechanism Calculator

BHMcalc provides renditions of the instantaneous circumbinary habital zone (CHZ) and also calculates BHM properties of the system including those related to the rotational evolution of the stellar components and the combined XUV and SW fluxes as measured at different distances from the binary. Moreover, it provides numerical results that can be further manipulated and used to calculate other properties.

[ascl:2105.001]
BHPToolkit: Black Hole Perturbation Toolkit

The Black Hole Perturbation Toolkit models gravitational radiation from small mass-ratio binaries as well as from the ringdown of black holes. The former are key sources for the future space-based gravitational wave detector LISA. BHPToolkit brings together core elements of multiple scattered black hole perturbation theory codes into a Toolkit that can be used by all; different tools can be installed individually by users depending on need and interest.

[ascl:9910.006]
BHSKY: Visual distortions near a black hole

BHSKY (copyright 1999 by Robert J. Nemiroff) computes the visual distortion effects visible to an observer traveling around and descending near a non-rotating black hole. The codes are general relativistically accurate and incorporate concepts such as large-angle deflections, image magnifications, multiple imaging, blue-shifting, and the location of the photon sphere. Once star.dat is edited to define the position and orientation of the observer relative to the black hole, bhsky_table should be run to create a table of photon deflection angles. Next bhsky_image reads this table and recomputes the perceived positions of stars in star.num, the Yale Bright Star Catalog. Lastly, bhsky_camera plots these results. The code currently tracks only the two brightest images of each star, and hence becomes noticeably incomplete within 1.1 times the Schwarzschild radius.

[ascl:1501.009]
BIANCHI: Bianchi VIIh Simulations

BIANCHI provides functionality to support the simulation of Bianchi Type VIIh induced temperature fluctuations in CMB maps of a universe with shear and rotation. The implementation is based on the solutions to the Bianchi models derived by Barrow et al. (1985), which do not incorporate any dark energy component. Functionality is provided to compute the induced fluctuations on the sphere directly in either real or harmonic space.

[ascl:1908.021]
bias_emulator: Halo bias emulator

bias_emulator models the clustering of halos on large scales. It incorporates the cosmological dependence of the bias beyond the mapping of halo mass to peak height. Precise measurements of the halo bias in the simulations are interpolated across cosmological parameter space to obtain the halo bias at any point in parameter space within the simulation cloud. A tool to produce realizations of correlated noise for propagating the modeling uncertainty into error budgets that use the emulator is also provided.

[ascl:1312.004]
BIE: Bayesian Inference Engine

The Bayesian Inference Engine (BIE) is an object-oriented library of tools written in C++ designed explicitly to enable Bayesian update and model comparison for astronomical problems. To facilitate "what if" exploration, BIE provides a command line interface (written with Bison and Flex) to run input scripts. The output of the code is a simulation of the Bayesian posterior distribution from which summary statistics e.g. by taking moments, or determine confidence intervals and so forth, can be determined. All of these quantities are fundamentally integrals and the Markov Chain approach produces variates $ heta$ distributed according to $P( heta|D)$ so moments are trivially obtained by summing of the ensemble of variates.

[ascl:1711.021]
Bifrost: Stream processing framework for high-throughput applications

Bifrost is a stream processing framework that eases the development of high-throughput processing CPU/GPU pipelines. It is designed for digital signal processing (DSP) applications within radio astronomy. Bifrost uses a flexible ring buffer implementation that allows different signal processing blocks to be connected to form a pipeline. Each block may be assigned to a CPU core, and the ring buffers are used to transport data to and from blocks. Processing blocks may be run on either the CPU or GPU, and the ring buffer will take care of memory copies between the CPU and GPU spaces.

[ascl:1208.007]
Big MACS: Accurate photometric calibration

Kelly, P. L.; von der Linden, A.; Applegate, D.; Allen, M.; Allen, S. W.; Burchat, P. R.; Burke, D. L.; Ebeling, H.; Capak, P.; Czoske, O.; Donovan, D.; Mantz, A.; Morris, R. G.

Big MACS is a Python program that estimates an accurate photometric calibration from only an input catalog of stellar magnitudes and filter transmission functions. The user does not have to measure color terms which can be difficult to characterize. Supplied with filter transmission functions, Big MACS synthesizes an expected stellar locus for your data and then simultaneously solves for all unknown zeropoints when fitting to the instrumental locus. The code uses a spectroscopic model for the SDSS stellar locus in color-color space and filter functions to compute expected locus. The stellar locus model is corrected for Milky Way reddening. If SDSS or 2MASS photometry is available for stars in field, Big MACS can yield a highly accurate absolute calibration.

[ascl:1901.011]
Bilby: Bayesian inference library

Ashton, Gregory; Hübner, Moritz; Lasky, Paul D.; Talbot, Colm; Ackley, Kendall; Biscoveanu, Sylvia; Chu, Qi; Divarkala, Atul; Easter, Paul J.; Goncharov, Boris; Hernandez Vivanco, Francisco; Harms, Jan; Lower, Marcus E.; Meadors, Grant D.; Melchor, Denyz; Payne, Ethan; Pitkin, Matthew D.; Powell, Jade,; Sarin, Nikhil; Smith, Rory J. E.; Thrane, Eric

Bilby provides a user-friendly interface to perform parameter estimation. It is primarily designed and built for inference of compact binary coalescence events in interferometric data, such as analysis of compact binary mergers and other types of signal model including supernovae and the remnants of binary neutron star mergers, but it can also be used for more general problems. The software is flexible, allowing the user to change the signal model, implement new likelihood functions, and add new detectors. Bilby can also be used to do population studies using hierarchical Bayesian modelling.

[ascl:2009.025]
Binary-Speckle: Binary or triple star parameters

Binary-Speckle reduces Speckle or AO data from the raw data to deconvolved images (in Fourier space), to determine the parameters of a binary or triple, and to find limits for undetected companion stars.

[ascl:1710.008]
Binary: Accretion disk evolution

Binary computes the evolution of an accretion disc interacting with a binary system. It has been developed and used to study the coupled evolution of supermassive BH binaries and gaseous accretion discs.

[ascl:1811.003]
binaryBHexp: On-the-fly visualizations of precessing binary black holes

binaryBHexp (binary black hole explorer) uses surrogate models of numerical simulations to generate on-the-fly interactive visualizations of precessing binary black holes. These visualizations can be generated in a few seconds and at any point in the 7-dimensional parameter space of the underlying surrogate models. These visualizations provide a valuable means to understand and gain insights about binary black hole systems and gravitational physics such as those detected by the LIGO gravitational wave detector.

[ascl:2102.025]
binaryoffset: Detecting and correcting the binary offset effect in CCDs

Boone, K.; Aldering, G.; Copin, Y.; Dixon, S.; Domagalski, R. S.; Gangler, E.; Pecontal, E.; Perlmutter, S.

binaryoffset identifies the binary offset effect in images from any detector. The easiest input to work with is a dark or bias image that is spatially flat. The code can also be run on images that are not spatially flat, assuming that there is some model of the signal on the CCD that can be used to produce a residual image.

[ascl:2012.004]
BinaryStarSolver: Orbital elements of binary stars solver

Given a series of radial velocities as a function of time for a star in a binary system, BinaryStarSolver solves for various orbital parameters. Namely, it solves for eccentricity (e), argument of periastron (ω), velocity amplitude (K), long term average radial velocity (γ), and orbital period (P). If the orbital parameters of a primary star are already known, it can also find the orbital parameters of a companion star, with only a few radial velocity data points.

[ascl:1312.012]
BINGO: BI-spectra and Non-Gaussianity Operator

The BI-spectra and Non-Gaussianity Operator (BINGO) code, written in Fortran, computes the scalar bi-spectrum and the non-Gaussianity parameter fNL in single field inflationary models involving the canonical scalar field. BINGO can calculate all the different contributions to the bi-spectrum and the parameter fNL for an arbitrary triangular configuration of the wavevectors.

[ascl:1805.015]
BinMag: Widget for comparing stellar observed with theoretical spectra

BinMag examines theoretical stellar spectra computed with Synth/SynthMag/Synmast/Synth3/SME spectrum synthesis codes and compare them to observations. An IDL widget program, BinMag applies radial velocity shift and broadening to the theoretical spectra to account for the effects of stellar rotation, radial-tangential macroturbulence, and instrumental smearing. The code can also simulate spectra of spectroscopic binary stars by appropriate coaddition of two synthetic spectra. Additionally, BinMag can be used to measure equivalent width, fit line profile shapes with analytical functions, and to automatically determine radial velocity and broadening parameters. BinMag interfaces with the Synth3 (ascl:1212.010) and SME (ascl:1202.013) codes, allowing the user to determine chemical abundances and stellar atmospheric parameters from the observed spectra.

[ascl:1905.004]
Binospec: Data reduction pipeline for the Binospec imaging spectrograph

Kansky, Jan; Chilingarian, Igor; Fabricant, Daniel; Matthews, Anne; Moran, Sean; Paegert, Martin; Duane Gibson, J.; Porter, Dallan; Roll, John

Binospec reduces data for the Binospec imaging spectrograph. The software is also used for observation planning and instrument control, and is automated to decrease the number of tasks the user has to perform. Binospec uses a database-driven approach for instrument configuration and sequencing of observations to maximize efficiency, and a web-based interface is available for defining observations, monitoring status, and retrieving data products.

[ascl:1011.008]
Binsim: Visualising Interacting Binaries in 3D

Binsim produces images of interacting binaries for any system parameters. Though not suitable for modeling light curves or spectra, the resulting images are helpful in visualizing the geometry of a given system and are also helpful in talks and educational work. The code uses the OpenGL API to do the 3D rendering. The software can produce images of cataclysmic variables and X-ray binaries, and can render the mass donor star, an axisymmetric disc (without superhumps, warps or spirals), the accretion stream and hotspot, and a "corona."

[ascl:1208.002]
BINSYN: Simulating Spectra and Light Curves of Binary Systems with or without Accretion Disks

The BINSYN program suite is a collection of programs for analysis of binary star systems with or without an optically thick accretion disk. BINSYN produces synthetic spectra of individual binary star components plus a synthetic spectrum of the system. If the system includes an accretion disk, BINSYN also produces a separate synthetic spectrum of the disk face and rim. A system routine convolves the synthetic spectra with filter profiles of several photometric standards to produce absolute synthetic photometry output. The package generates synthetic light curves and determines an optimized solution for system parameters.

[submitted]
BiPoS1 - a computer programme for the dynamical processing of the initial binary star population

This is the first version of the Binary Population Synthesizer (BiPoS1). It allows to efficiently calculate binary distribution functions after the dynamical processing of a realistic population of binary stars during the first few Myr in the hosting embedded star cluster. It is particularly useful for generating a realistic birth binary population as an input for N-body simulations of globular clusters. Instead of time-consuming N-body simulations, BiPoS1 uses the stellar dynamical operator, which determines the fraction of surviving binaries depending on the binding energy of the binaries. The stellar dynamical operator depends on the initial star cluster density, as well as the time until the residual gas of the star cluster is expelled. At the time of gas expulsion, the dynamical processing of the binary population is assumed to effectively end due to the expansion of the star cluster related to that event. BiPoS1 has also a galactic-field mode, in order to synthesize the stellar population of a whole galaxy.

[ascl:1512.008]
Bisous model: Detecting filamentary pattern in point processes

The Bisous model is a marked point process that models multi-dimensional patterns. The Bisous filament finder works directly with galaxy distribution data and the model intrinsically takes into account the connectivity of the filamentary network. The Bisous model generates the visit map (the probability to find a filament at a given point) together with the filament orientation field; these two fields are used to extract filament spines from the data.

[ascl:1712.004]
Bitshuffle: Filter for improving compression of typed binary data

Bitshuffle rearranges typed, binary data for improving compression; the algorithm is implemented in a python/C package within the Numpy framework. The library can be used alongside HDF5 to compress and decompress datasets and is integrated through the dynamically loaded filters framework. Algorithmically, Bitshuffle is closely related to HDF5's Shuffle filter except it operates at the bit level instead of the byte level. Arranging a typed data array in to a matrix with the elements as the rows and the bits within the elements as the columns, Bitshuffle "transposes" the matrix, such that all the least-significant-bits are in a row, etc. This transposition is performed within blocks of data roughly 8kB long; this does not in itself compress data, but rearranges it for more efficient compression. A compression library is necessary to perform the actual compression. This scheme has been used for compression of radio data in high performance computing.

[ascl:1411.027]
BKGE: Fermi-LAT Background Estimator

The Fermi-LAT Background Estimator (BKGE) is a publicly available open-source tool that can estimate the expected background of the Fermi-LAT for any observational conguration and duration. It produces results in the form of text files, ROOT files, gtlike source-model files (for LAT maximum likelihood analyses), and PHA I/II FITS files (for RMFit/XSpec spectral fitting analyses). Its core is written in C++ and its user interface in Python.

[ascl:2105.011]
BlackBOX: BlackGEM and MeerLICHT image reduction software

BlackBOX performs standard CCD image reduction tasks on multiple images from the BlackGEM and MeerLICHT telescopes. It uses the satdet module of ASCtools (ascl:2011.024) and Astro-SCRAPPY (ascl:1907.032). BlackBOX simultaneously uses multi-processing and multi-threading and feeds the reduced images to ZOGY (ascl:2105.010) to ultimately perform optimal image subtraction and detect transient sources.

[ascl:2012.020]
BlackHawk: Black hole evaporation calculator

BlackHawk calculates the Hawking evaporation spectra of any black hole distribution. Written in C, the program enables users to compute the primary and secondary spectra of stable or long-lived particles generated by Hawking radiation of the distribution of black holes, and to study their evolution in time.

[ascl:1906.002]
Blimpy: Breakthrough Listen I/O Methods for Python

Blimpy (Breakthrough Listen I/O Methods for Python) provides utilities for viewing and interacting with the data formats used within the Breakthrough Listen program, including Sigproc filterbank (.fil) and HDF5 (.h5) files that contain dynamic spectra (aka 'waterfalls'), and guppi raw (.raw) files that contain voltage-level data. Blimpy can also extract, calibrate, and visualize data and a suite of command-line utilities are also available.

[ascl:1208.009]
BLOBCAT: Software to Catalog Blobs

BLOBCAT is a source extraction software that utilizes the flood fill algorithm to detect and catalog blobs, or islands of pixels representing sources, in 2D astronomical images. The software is designed to process radio-wavelength images of both Stokes I intensity and linear polarization, the latter formed through the quadrature sum of Stokes Q and U intensities or as a by-product of rotation measure synthesis. BLOBCAT corrects for two systematic biases to enable the flood fill algorithm to accurately measure flux densities for Gaussian sources. BLOBCAT exhibits accurate measurement performance in total intensity and, in particular, linear polarization, and is particularly suited to the analysis of large survey data.

[ascl:9909.005]
BLOCK: A Bayesian block method to analyze structure in photon counting data

Bayesian Blocks is a time-domain algorithm for detecting localized structures (bursts), revealing pulse shapes, and generally characterizing intensity variations. The input is raw counting data, in any of three forms: time-tagged photon events, binned counts, or time-to-spill data. The output is the most probable segmentation of the observation into time intervals during which the photon arrival rate is perceptibly constant, i.e. has no statistically significant variations. The idea is not that the source is deemed to have this discontinuous, piecewise constant form, rather that such an approximate and generic model is often useful. The analysis is based on Bayesian statistics.

This code is obsolete and yields approximate results; see Bayesian Blocks instead for an algorithm guaranteeing exact global optimization.

[ascl:1607.008]
BLS: Box-fitting Least Squares

BLS (Box-fitting Least Squares) is a box-fitting algorithm that analyzes stellar photometric time series to search for periodic transits of extrasolar planets. It searches for signals characterized by a periodic alternation between two discrete levels, with much less time spent at the lower level.

[ascl:1709.009]
bmcmc: MCMC package for Bayesian data analysis

bmcmc is a general purpose Markov Chain Monte Carlo package for Bayesian data analysis. It uses an adaptive scheme for automatic tuning of proposal distributions. It can also handle Bayesian hierarchical models by making use of the Metropolis-Within-Gibbs scheme.

[ascl:1801.008]
BOND: Bayesian Oxygen and Nitrogen abundance Determinations

BOND determines oxygen and nitrogen abundances in giant H II regions by comparison with a large grid of photoionization models. The grid spans a wide range in O/H, N/O and ionization parameter U, and covers different starburst ages and nebular geometries. Unlike other statistical methods, BOND relies on the [Ar III]/[Ne III] emission line ratio to break the oxygen abundance bimodality. By doing so, it can measure oxygen and nitrogen abundances without assuming any a priori relation between N/O and O/H. BOND takes into account changes in the hardness of the ionizing radiation field, which can come about due to the ageing of H II regions or the stochastically sampling of the IMF. The emission line ratio He I/Hβ, in addition to commonly used strong lines, constrains the hardness of the ionizing radiation field. BOND relies on the emission line ratios [O III]/Hβ, [O II]/Hβ and [N II]/Hβ, [Ar III]/Hβ, [Ne III]/Hβ, He I/Hβ as its input parameters, while its output values are the measurements and uncertainties for O/H and N/O.

[ascl:1212.001]
Bonsai: N-body GPU tree-code

Bonsai is a gravitational N-body tree-code that runs completely on the GPU. This reduces the amount of time spent on communication with the CPU. The code runs on NVIDIA GPUs and on a GTX480 it is able to integrate ~2.8M particles per second. The tree construction and traverse algorithms are portable to many-core devices which have support for CUDA or OpenCL programming languages.

[ascl:1210.030]
BOOTTRAN: Error Bars for Keplerian Orbital Parameters

BOOTTRAN calculates error bars for Keplerian orbital parameters for both single- and multiple-planet systems. It takes the best-fit parameters and radial velocity data (BJD, velocity, errors) and calculates the error bars from sampling distribution estimated via bootstrapping. It is recommended to be used together with the RVLIN (ascl:1210.031) package, which find best-fit Keplerian orbital parameters. Both RVLIN and BOOTTRAN are compatible with multiple-telescope data. BOOTTRAN also calculates the transit time and secondary eclipse time and their associated error bars. The algorithm is described in the appendix of the associated article.

[ascl:1108.019]
BOREAS: Mass Loss Rate of a Cool, Late-type Star

The basic mechanisms responsible for producing winds from cool, late-type stars are still largely unknown. We take inspiration from recent progress in understanding solar wind acceleration to develop a physically motivated model of the time-steady mass loss rates of cool main-sequence stars and evolved giants. This model follows the energy flux of magnetohydrodynamic turbulence from a subsurface convection zone to its eventual dissipation and escape through open magnetic flux tubes. We show how Alfven waves and turbulence can produce winds in either a hot corona or a cool extended chromosphere, and we specify the conditions that determine whether or not coronal heating occurs. These models do not utilize arbitrary normalization factors, but instead predict the mass loss rate directly from a star's fundamental properties. We take account of stellar magnetic activity by extending standard age-activity-rotation indicators to include the evolution of the filling factor of strong photospheric magnetic fields. We compared the predicted mass loss rates with observed values for 47 stars and found significantly better agreement than was obtained from the popular scaling laws of Reimers, Schroeder, and Cuntz. The algorithm used to compute cool-star mass loss rates is provided as a self-contained and efficient IDL computer code. We anticipate that the results from this kind of model can be incorporated straightforwardly into stellar evolution calculations and population synthesis techniques.

[ascl:1607.017]
BoxRemap: Volume and local structure preserving mapping of periodic boxes

BoxRemap remaps the cubical domain of a cosmological simulation into simple non-cubical shapes. It can be used for on-the-fly remappings of the simulation geometry and is volume-preserving; remapped geometry has the same volume V = L3 as the original simulation box. The remappings are structure-preserving (local neighboring structures are mapped to neighboring places) and one-to-one, with every particle/halo/galaxy/etc. appearing once and only once in the remapped volume.

[ascl:1108.011]
BPZ: Bayesian Photometric Redshift Code

Photometric redshift estimation is becoming an increasingly important technique, although the currently existing methods present several shortcomings which hinder their application. Most of those drawbacks are efficiently eliminated when Bayesian probability is consistently applied to this problem. The use of prior probabilities and Bayesian marginalization allows the inclusion of valuable information, e.g. the redshift distributions or the galaxy type mix, which is often ignored by other methods. In those cases when the a priori information is insufficient, it is shown how to `calibrate' the prior distributions, using even the data under consideration. There is an excellent agreement between the 108 HDF spectroscopic redshifts and the predictions of the method, with a rms error Delta z/(1+z_spec) = 0.08 up to z<6 and no systematic biases nor outliers. The results obtained are more reliable than those of standard techniques even when the latter include near-IR colors. The Bayesian formalism developed here can be generalized to deal with a wide range of problems which make use of photometric redshifts, e.g. the estimation of individual galaxy characteristics as the metallicity, dust content, etc., or the study of galaxy evolution and the cosmological parameters from large multicolor surveys. Finally, using Bayesian probability it is possible to develop an integrated statistical method for cluster mass reconstruction which simultaneously considers the information provided by gravitational lensing and photometric redshifts.

[ascl:1806.025]
BRATS: Broadband Radio Astronomy ToolS

BRATS (Broadband Radio Astronomy ToolS) provides tools for the spectral analysis of broad-bandwidth radio data and legacy support for narrowband telescopes. It can fit models of spectral ageing on small spatial scales, offers automatic selection of regions based on user parameters (e.g. signal to noise), and automatic determination of the best-fitting injection index. It includes statistical testing, including Chi-squared, error maps, confidence levels and binning of model fits, and can map spectral index as a function of position. It also provides the ability to reconstruct sources at any frequency for a given model and parameter set, subtract any two FITS images and output residual maps, easily combine and scale FITS images in the image plane, and resize radio maps.

[ascl:1412.005]
BRUCE/KYLIE: Pulsating star spectra synthesizer

BRUCE and KYLIE, written in Fortran 77, synthesize the spectra of pulsating stars. BRUCE constructs a point-sampled model for the surface of a rotating, gravity-darkened star, and then subjects this model to perturbations arising from one or more non-radial pulsation modes. Departures from adiabaticity can be taken into account, as can the Coriolis force through adoption of the so-called traditional approximation. BRUCE writes out a time-sequence of perturbed surface models. This sequence is read in by KYLIE, which synthesizes disk-integrated spectra for the models by co-adding the specific intensity emanating from each visible point toward the observer. The specific intensity is calculated by interpolation in a large temperature-gravity-wavelength-angle grid of pre-calculated intensity spectra.

[ascl:1407.016]
Brut: Automatic bubble classifier

Brut, written in Python, identifies bubbles in infrared images of the Galactic midplane; it uses a database of known bubbles from the Milky Way Project and Spitzer images to build an automatic bubble classifier. The classifier is based on the Random Forest algorithm, and uses the WiseRF implementation of this algorithm.

[ascl:1903.004]
brutifus: Python module to post-process datacubes from integral field spectrographs

brutifus aids in post-processing datacubes from integral field spectrographs. The set of Python routines in the package handle generic tasks, such as the registration of a datacube WCS solution with the Gaia catalogue, the correction of Galactic reddening, or the subtraction of the nebular/stellar continuum on a spaxel-per-spaxel basis, with as little user interactions as possible. brutifus is modular, in that the order in which the post-processing routines are run is entirely customizable.

[ascl:1303.014]
BSE: Binary Star Evolution

BSE is a rapid binary star evolution code. It can model circularization of eccentric orbits and synchronization of stellar rotation with the orbital motion owing to tidal interaction in detail. Angular momentum loss mechanisms, such as gravitational radiation and magnetic braking, are also modelled. Wind accretion, where the secondary may accrete some of the material lost from the primary in a wind, is allowed with the necessary adjustments made to the orbital parameters in the event of any mass variations. Mass transfer occurs if either star fills its Roche lobe and may proceed on a nuclear, thermal or dynamical time-scale. In the latter regime, the radius of the primary increases in response to mass-loss at a faster rate than the Roche-lobe of the star. Prescriptions to determine the type and rate of mass transfer, the response of the secondary to accretion and the outcome of any merger events are in place in BSE.

[ascl:9904.001]
BSGMODEL: The Bahcall-Soneira Galaxy Model

BSGMODEL is used to construct the disk and spheroid components of the Galaxy from which the distribution of visible stars and mass in the Galaxy is calculated. The computer files accessible here are available for export use. The modifications are described in comment lines in the software. The Galaxy model software has been installed and used by different people for a large variety of purposes (see, e. g., the the review "Star Counts and Galactic Structure'', Ann. Rev. Astron. Ap. 24, 577, 1986 ).

[ascl:2001.007]
BTS: Behind The Spectrum

Clarke, S. D.; Whitworth, A. P.; Spowage, R. L.; Duarte-Cabral, A.; Suri, S. T.; Jaffa, S. E.; Walch, S.; Clark, P. C.

Behind The Spectrum (BTS) is a fully-automated multiple-component fitter for optically-thin spectra. Written as a python module, the routine uses the first, second and third derivatives to determine thenumber of components in the spectrum. A least-squared fitting routine then determines the best fit with that number of components, checking for over-fitting and over-lapping velocity centroids.

[ascl:1204.003]
BUDDA: BUlge/Disk Decomposition Analysis

Budda is a Fortran code developed to perform a detailed structural analysis on galaxy images. It is simple to use and gives reliable estimates of the galaxy structural parameters, which can be used, for instance, in Fundamental Plane studies. Moreover, it has a powerful ability to reveal hidden sub-structures, like inner disks, secondary bars and nuclear rings.

[ascl:1610.010]
BurnMan: Lower mantle mineral physics toolkit

BurnMan determines seismic velocities for the lower mantle. Written in Python, BurnMan calculates the isotropic thermoelastic moduli by solving the equations-of-state for a mixture of minerals defined by the user. The user may select from a list of minerals applicable to the lower mantle included or can define one. BurnMan provides choices in methodology, both for the EoS and for the multiphase averaging scheme and the results can be visually or quantitatively compared to observed seismic models.

[ascl:1806.026]
BWED: Brane-world extra dimensions

Braneworld-extra-dimensions places constraints on the size of the AdS5 radius of curvature within the Randall-Sundrum brane-world model in light of the near-simultaneous detection of the gravitational wave event GW170817 and its optical counterpart, the short γ-ray burst event GRB170817A. The code requires a (supplied) patch to the Montepython cosmological MCMC sampler (ascl:1805.027) to sample the posterior distribution of the 4-dimensional parameter space in VBV17 and obtain constraints on the parameters.

[ascl:1610.011]
BXA: Bayesian X-ray Analysis

BXA connects the nested sampling algorithm MultiNest (ascl:1109.006) to the X-ray spectral analysis environments Xspec (ascl:9910.005) and Sherpa (ascl:1107.005) for Bayesian parameter estimation and model comparison. It provides parameter estimation in arbitrary dimensions and plotting of spectral model vs. the data for best fit, posterior samples, or each component. BXA allows for model selection; it computes the evidence for the considered model, ready for use in computing Bayes factors and is not limited to nested models. It also visualizes deviations between model and data with Quantile-Quantile (QQ) plots, which do not require binning and are more comprehensive than residuals.

[ascl:1211.005]
C-m Emu: Concentration-mass relation emulator

The concentration-mass relation for dark matter-dominated halos is one of the essential results expected from a theory of structure formation. C-m Emu is a simple numerical code for the c-M relation as a function of cosmological parameters for wCDM models generates the best-fit power-law model for each redshift separately and then interpolate between the redshifts. This produces a more accurate answer at each redshift at the minimal cost of running a fast code for every c -M prediction instead of using one fitting formula. The emulator is constructed from 37 individual models, with three nested N-body gravity-only simulations carried out for each model. The mass range covered by the emulator is 2 x 10^{12} M_sun < M <10^{15} M_sun with a corresponding redshift range of z=0 -1. Over this range of mass and redshift, as well as the variation of cosmological parameters studied, the mean halo concentration varies from c ~ 2 to c ~ 8. The distribution of the concentration at fixed mass is Gaussian with a standard deviation of one-third of the mean value, almost independent of cosmology, mass, and redshift over the ranges probed by the simulations.

[ascl:1610.006]
C^{3}: Command-line Catalogue Crossmatch for modern astronomical surveys

The Command-line Catalogue Cross-matching (C^{3}) software efficiently performs the positional cross-match between massive catalogues from modern astronomical surveys, whose size have rapidly increased in the current data-driven science era. Based on a multi-core parallel processing paradigm, it is executed as a stand-alone command-line process or integrated within any generic data reduction/analysis pipeline. C^{3} provides its users with flexibility in portability, parameter configuration, catalogue formats, angular resolution, region shapes, coordinate units and cross-matching types.

[ascl:1102.013]
Cactus: HPC infrastructure and programming tools

Cactus provides computational scientists and engineers with a collaborative, modular and portable programming environment for parallel high performance computing. Cactus can make use of many other technologies for HPC, such as Samrai, HDF5, PETSc and PAPI, and several application domains such as numerical relativity, computational fluid dynamics and quantum gravity are developing open community toolkits for Cactus.

[ascl:1303.017]
CADRE: CArma Data REduction pipeline

CADRE, the Combined Array for Millimeter-wave Astronomy (CARMA) data reduction pipeline, gives investigators a first look at a fully reduced set of their data. It runs automatically on all data produced by the telescope as they arrive in the data archive. The pipeline is written in python and uses python wrappers for MIRIAD subroutines for direct access to the data. It applies passband, gain and flux calibration to the data sets and produces a set of continuum and spectral line maps in both MIRIAD and FITS format.

[submitted]
caesar-rest

caesar-rest is a REST-ful web service for astronomical source extraction and classification with the caesar source extractor [ascl:1807.015]. The software is developed in python and consists of a few containerized microservices, deployable on standalone servers or on a distributed cloud infrastructure. The core component is the REST web application, based on the Flask framework and providing APIs for managing the input data (e.g. data upload/download/removal) and source finding jobs (e.g. submit, get status, get outputs) with different job management systems (Kubernetes, Slurm, Celery). Additional services (AAI, user DB, log storage, job monitor, accounting) enable the user authentication, the storage and retrieval of user data and job information, the monitoring of submitted jobs, and the aggregation of service logs and user data/job stats.

Besides caesar, we also foresee to integrate other tools widely used in the radio community (e.g. Aegean, PyBDSF) and newly developed source finders based on deep learning models.

[ascl:1807.015]
CAESAR: Compact And Extended Source Automated Recognition

CAESAR extracts and parameterizes both compact and extended sources from astronomical radio interferometric maps. The processing pipeline is a series of stages that can run on multiple cores and processors. After local background and rms map computation, compact sources are extracted with flood-fill and blob finder algorithms, processed (selection + deblending), and fitted using a 2D gaussian mixture model. Extended source search is based on a pre-filtering stage, allowing image denoising, compact source removal and enhancement of diffuse emission, followed by a final segmentation. Different algorithms are available for image filtering and segmentation. The outputs delivered to the user include source fitted and shape parameters, regions and contours. Written in C++, CAESAR is designed to handle the large-scale surveys planned with the Square Kilometer Array (SKA) and its precursors.

[ascl:1505.001]
CALCEPH: Planetary ephemeris files access code

CALCEPH accesses binary planetary ephemeris files, including INPOPxx, JPL DExxx ,and SPICE ephemeris files. It provides a C Application Programming Interface (API) and, optionally, a Fortran 77 or 2003 interface to be called by the application. Two groups of functions enable the access to the ephemeris files, single file access functions, provided to make transition easier from the JPL functions, such as PLEPH, to this library, and many ephemeris file at the same time. Although computers have different endianess (order in which integers are stored as bytes in computer memory), CALCEPH can handles the binary ephemeris files with any endianess by automatically swaps the bytes when it performs read operations on the ephemeris file.

[ascl:1210.010]
CALCLENS: Curved-sky grAvitational Lensing for Cosmological Light conE simulatioNS

CALCLENS, written in C and employing widely available software libraries, efficiently computes weak gravitational lensing shear signals from large N-body light cone simulations over a curved sky. The algorithm properly accounts for the sky curvature and boundary conditions, is able to produce redshift-dependent shear signals including corrections to the Born approximation by using multiple-plane ray tracing, and properly computes the lensed images of source galaxies in the light cone. The key feature of this algorithm is a new, computationally efficient Poisson solver for the sphere that combines spherical harmonic transform and multgrid methods. As a result, large areas of sky (~10,000 square degrees) can be ray traced efficiently at high-resolution using only a few hundred cores on widely available machines. Coupled with realistic galaxy populations placed in large N-body light cone simulations, CALCLENS is ideally suited for the construction of synthetic weak lensing shear catalogs to be used to test for systematic effects in data analysis procedures for upcoming large-area sky surveys.

[ascl:1105.013]
CAMB Sources: Number Counts, Lensing & Dark-age 21cm Power Spectra

We relate the observable number of sources per solid angle and redshift to the underlying proper source density and velocity, background evolution and line-of-sight potentials. We give an exact result in the case of linearized perturbations assuming general relativity. This consistently includes contributions of the source density perturbations and redshift distortions, magnification, radial displacement, and various additional linear terms that are small on sub-horizon scales. In addition we calculate the effect on observed luminosities, and hence the result for sources observed as a function of flux, including magnification bias and radial-displacement effects. We give the corresponding linear result for a magnitude-limited survey at low redshift, and discuss the angular power spectrum of the total count distribution. We also calculate the cross-correlation with the CMB polarization and temperature including Doppler source terms, magnification, redshift distortions and other velocity effects for the sources, and discuss why the contribution of redshift distortions is generally small. Finally we relate the result for source number counts to that for the brightness of line radiation, for example 21-cm radiation, from the sources.

[ascl:1102.026]
CAMB: Code for Anisotropies in the Microwave Background

We present a fully covariant and gauge-invariant calculation of the evolution of anisotropies in the cosmic microwave background (CMB) radiation. We use the physically appealing covariant approach to cosmological perturbations, which ensures that all variables are gauge-invariant and have a clear physical interpretation. We derive the complete set of frame-independent, linearised equations describing the (Boltzmann) evolution of anisotropy and inhomogeneity in an almost Friedmann-Robertson-Walker (FRW) cold dark matter (CDM) universe. These equations include the contributions of scalar, vector and tensor modes in a unified manner. Frame-independent equations for scalar and tensor perturbations, which are valid for any value of the background curvature, are obtained straightforwardly from the complete set of equations. We discuss the scalar equations in detail, including the integral solution and relation with the line of sight approach, analytic solutions in the early radiation dominated era, and the numerical solution in the standard CDM model. Our results confirm those obtained by other groups, who have worked carefully with non-covariant methods in specific gauges, but are derived here in a completely transparent fashion.

[ascl:1801.007]
cambmag: Magnetic Fields in CAMB

cambmag is a modification to CAMB (ascl:1102.026) that calculates the compensated magnetic mode in the scalar, vector and tensor case. Previously CAMB included code only for the vectors. It also corrects for tight-coupling issues and adds in the ability to include massive neutrinos when calculating vector modes.

[ascl:1605.006]
CAMELOT: Cloud Archive for MEtadata, Library and Online Toolkit

Ginsburg, Adam; Kruijssen, J. M. Diederik; Longmore, Steven N.; Koch, Eric; Glover, Simon C. O.; Dale, James E.; Commerçon, Benoît; Giannetti, Andrea; McLeod, Anna F.; Testi, Leonardo; Zahorecz, Sarolta; Rathborne, Jill M.; Zhang, Qizhou; Fontani, Francesco; Beltrán, Maite T.; Rivilla, Victor M.

CAMELOT facilitates the comparison of observational data and simulations of molecular clouds and/or star-forming regions. The central component of CAMELOT is a database summarizing the properties of observational data and simulations in the literature through pertinent metadata. The core functionality allows users to upload metadata, search and visualize the contents of the database to find and match observations/simulations over any range of parameter space.

To bridge the fundamental disconnect between inherently 2D observational data and 3D simulations, the code uses key physical properties that, in principle, are straightforward for both observers and simulators to measure — the surface density (Sigma), velocity dispersion (sigma) and radius (R). By determining these in a self-consistent way for all entries in the database, it should be possible to make robust comparisons.

[ascl:1502.015]
Camelus: Counts of Amplified Mass Elevations from Lensing with Ultrafast Simulations

Camelus provides a prediction on weak lensing peak counts from input cosmological parameters. Written in C, it samples halos from a mass function and assigns a profile, carries out ray-tracing simulations, and then counts peaks from ray-tracing maps. The creation of the ray-tracing simulations requires less computing time than N-body runs and the results is in good agreement with full N-body simulations.

[ascl:1505.030]
CANDID: Companion Analysis and Non-Detection in Interferometric Data

Gallenne, A.; Mérand, A.; Kervella, P.; Monnier, J. D.; Schaefer, G. H.; Baron, F.; Breitfelder, J.; Le Bouquin, J. B.; Roettenbacher, R. M.; Gieren, W.; Pietrzynski, G.; McAlister, H.; ten Brummelaar, T.; Sturmann, J.; Sturmann, L.; Turner, N.; Ridgway, S.; Kraus, S.

CANDID finds faint companion around star in interferometric data in the OIFITS format. It allows systematically searching for faint companions in OIFITS data, and if not found, estimates the detection limit. The tool is based on model fitting and Chi2 minimization, with a grid for the starting points of the companion position. It ensures all positions are explored by estimating a-posteriori if the grid is dense enough, and provides an estimate of the optimum grid density.

[ascl:1106.017]
CAOS: Code for Adaptive Optics Systems

The CAOS "system" (where CAOS stands for Code for Adaptive Optics Systems) is properly said a Problem Solving Environment (PSE). It is essentially composed of a graphical programming interface (the CAOS Application Builder) which can load different packages (set of modules). Current publicly distributed packages are the Software Package CAOS (the original adaptive optics package), the Software Package AIRY (an image-reconstruction-oriented package - AIRY stands for Astronomical Image Restoration with interferometrY), the Software Package PAOLAC (a simple CAOS interface for the analytic IDL code PAOLA developed by Laurent Jolissaint - PAOLAC stands for PAOLA within Caos), and a couple of private packages (not publicly distributed but restricted to the corresponding consortia): SPHERE (especially developed for the VLT planet finder SPHERE), and AIRY-LN (a specialized version of AIRY for the LBT instrument LINC-NIRVANA). Another package is also being developed: MAOS (that stands for Multiconjugate Adaptive Optics Simulations), developed for multi-reference multiconjugate AO studies purpose but still in a beta-version form.

[ascl:1404.011]
CAP_LOESS_1D & CAP_LOESS_2D: Recover mean trends from noisy data

CAP_LOESS_1D and CAP_LOESS_2D provide improved implementations of the one-dimensional (Clevelend 1979) and two-dimensional (Cleveland & Devlin 1988) Locally Weighted Regression (LOESS) methods to recover the mean trends of the population from noisy data in one or two dimensions. They include a robust approach to deal with outliers (bad data). The software is available in both IDL and Python versions.

[ascl:2011.002]
CAPTURE: Interferometric pipeline for image creation from GMRT data

CAPTURE (CAsa Pipeline-cum-Toolkit for Upgraded Giant Metrewave Radio Telescope data REduction) produces continuum images from radio interferometric data. Written in Python, it uses CASA (ascl:1107.013) tasks to analyze data obtained by the GMRT. It can produce self-calibrated images in a fully automatic mode or can run in steps to allow the data to be inspected throughout processing.

[ascl:2006.014]
CARACal: Containerized Automated Radio Astronomy Calibration pipeline

Józsa, Gyula I. G.; White, Sarah V.; Thorat, Kshitij; Smirnov, Oleg M.; Serra, Paolo; Ramatsoku, Mpati; Ramaila, Athanaseus J. T.; Perkins, Simon J.; Molnár, Dániel Cs.; Makhathini, Sphesihle; Maccagni, Filippo M.; Kleiner, Dane; Kamphuis, Peter; Hugo, Benjamin V.; de Blok, W. J. G.; Andati, Lexy A. L.

CARACal (Containerized Automated Radio Astronomy Calibration, formerly MeerKATHI) reduces radio-interferometric data. Developed originally as an end-to-end continuum- and line imaging pipeline for MeerKAT, it can also be used with other radio telescopes. CARACal reduces large data sets and produces high-dynamic-range continuum images and spectroscopic data cubes. The pipeline is platform-independent and delivers imaging quality metrics to efficiently assess the data quality.

[ascl:1505.003]
caret: Classification and Regression Training

caret (Classification And REgression Training) provides functions for training and plotting classification and regression models. It contains tools for data splitting, pre-processing, feature selection, model tuning using resampling, and variable importance estimation, as well as other functionality.

[ascl:1404.009]
carma_pack: MCMC sampler for Bayesian inference

carma_pack is an MCMC sampler for performing Bayesian inference on continuous time autoregressive moving average models. These models may be used to model time series with irregular sampling. The MCMC sampler utilizes an adaptive Metropolis algorithm combined with parallel tempering.

[ascl:1611.016]
Carpet: Adaptive Mesh Refinement for the Cactus Framework

Carpet is an adaptive mesh refinement and multi-patch driver for the Cactus Framework (ascl:1102.013). Cactus is a software framework for solving time-dependent partial differential equations on block-structured grids, and Carpet acts as driver layer providing adaptive mesh refinement, multi-patch capability, as well as parallelization and efficient I/O.

[ascl:2005.007]
Carpyncho: VVV Catalog browser toolkit

Carpyncho browses catalogs to search for and characterize time variable data of the Vista Variables in the Via Lactea (VVV) Survey. The stacked pawprint data from the Cambridge Astronomical Science Unit's (CASU) Vista Data Flow System (VDFS) v>= 1.3 catalogs have been crossed matched with the VDFS CASU v1.3 tile catalogs into Parquet files, allowing detection and classification of periodic variables within this dataset.

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