JUDE (Jayant's UVIT Data Explorer) converts the Level 1 data (FITS binary table) from the Ultraviolet Imaging Telescope (UVIT) on ASTROSAT into three output files: a photon event list as a function of frame number (FITS binary table); a FITS image file with two extensions; and a PNG file created from the FITS image file with an automated scaling.
Juliet essentially serves as a wrapper of other tools, including Batman (ascl:1510.002), George (ascl:1511.015), Dynesty (ascl:1809.013) and AstroPy (ascl:1304.002), to analyze and model transits, radial-velocities, or both from multiple instruments at the same time. Using nested sampling algorithms, it performs a thorough sampling of the parameter space and a model comparison via Bayesian evidences. Juliet also fits transiting and non-transiting multi-planetary systems, and Gaussian Processes (GPs) which might share hyperparameters between the photometry and radial-velocities simultaneously (e.g., stellar rotation periods).
Jupiter is a multidimensional astrophysical hydrocode. It is based on a Godunov method, and it is parallelized with MPI. The mesh geometry can either be cartesian, cylindrical or spherical. It allows mesh refinement and includes special features adapted to the description of planets embedded in disks and nearly steady states.
Juwvid performs time-frequency analysis. Written in Julia, it uses a modified version of the Wigner distribution, the pseudo Wigner distribution, and the short-time Fourier transform from MATLAB GPL programs, tftb-0.2. The modification includes the zero-padding FFT, the non-uniform FFT, the adaptive algorithm by Stankovic, Dakovic, Thayaparan 2013, the S-method, the L-Wigner distribution, and the polynomial Wigner-Ville distribution.
JVarStar (Java Variable Star Analysis) performs pattern classification by analyzing variable star data. This all-in-one library package includes machine learning techniques, fundamental mathematical methods, and digital signal processing functions that can be externally referenced (i.e., from Python), or can be used for further Java development. This library has dependencies on several open source packages that, along with the developed functionality, provides a developer with an easily accessible library from which to construct stable variable star analysis and classification code.
JWFront visualizes wavefronts and light cones in general relativity. The interactive front-end allows users to enter the initial position values and choose the values for mass and angular momentum per unit mass. The wavefront animations are available in 2D and 3D; the light cones are visualized using the coordinate systems (t, x, y) or (t, z, x). JWFront can be easily modified to simulate wavefronts and light cones for other spacetime by providing the Christoffel symbols in the program.
Inpainting is a technique for dealing with gaps in time series data, as frequently occurs in asteroseismology data, that may generate spurious peaks in the power spectrum, thus limiting the analysis of the data. The inpainting method, based on a sparsity prior, judiciously fills in gaps in the data, preserving the asteroseismic signal as far as possible. This method can be applied both on ground and space-based data. The inpainting technique improves the oscillation modes detection and estimation, the impact of the observational window function is reduced, and the interpretation of the power spectrum is simplified. K-Inpainting can be used to study very long time series of many stars because its computation is very fast.
Since early 2018, the Kepler/K2 project has been performing a uniform global reprocessing of data from K2 Campaigns 0 through 14. Subsequent K2 campaigns (C15-C19) are being processed using the same processing pipeline. One of the major benefits of the reprocessing effort is that, for the first time, short-cadence (1-min) light curves are produced in addition to the standard long-cadence (30-min) light curves. Users have been cautioned that the Kepler pipeline detrending module (PDC), developed for use on original Kepler data, has not been tailored for use on short-cadence K2 observations. Systematics due to events on fast timescales, such as thruster firings, are sometimes poorly corrected for many short-cadence targets. A Python data visualization and manipulation tool, called Kepler-K2 Cadence Events, has been developed that identifies and removes cadences associated with problematic thruster events, thus producing better light curves. Kepler-K2 Cadence Events can be used to visualize and manipulate light curve files and target pixel files from the Kepler, K2, and TESS missions. This software is available at the following NASA GitHub repository https://github.com/nasa/K2CE .
K2flix makes it easy to inspect the CCD pixel data obtained by NASA's Kepler space telescope. The two-wheeled extended Kepler mission, K2, is affected by new sources of systematics, including pointing jitter and foreground asteroids, that are easier to spot by eye than by algorithm. The code takes Kepler's Target Pixel Files (TPF) as input and turns them into contrast-stretched animated gifs or MPEG-4 movies. K2flix can be used both as a command-line tool or using its Python API.
K2fov allows users to transform celestial coordinates into K2's pixel coordinate system for the purpose of preparing target proposals and field of view visualizations. In particular, the package, written in Python, adds the "K2onSilicon" and "K2findCampaigns" tools to the command line, allowing the visibility of targets to be checked in a user-friendly way.
k2photometry reads, reduces and detrends K2 photometry and searches for transiting planets. MAST database pixel files are used as input; the output includes raw lightcurves, detrended lightcurves and a transit search can be performed as well. Stellar variability is not typically well-preserved but parameters can be tweaked to change that. The BLS algorithm used to detect periodic events is a Python implementation by Ruth Angus and Dan Foreman-Mackey (https://github.com/dfm/python-bls).
K2PS is an Oxford K2 planet search pipeline. Written in Python, it searches for transit-like signals from the k2sc-detrended light curves.
K2SC (K2 Systematics Correction) models instrumental systematics and astrophysical variability in light curves from the K2 mission. It enables the user to remove both position-dependent systematics and time-dependent variability (e.g., for transit searches) or to remove systematics while preserving variability (for variability studies). K2SC automatically computes estimates of the period, amplitude and evolution timescale of the variability for periodic variables and can be run on ASCII and FITS light curve files. Written in Python, this pipeline requires NumPy, SciPy, MPI4Py, Astropy (ascl:1304.002), and George (ascl:1511.015).
K3Match is a C library with Python bindings for fast matching of points in 3D space. It uses an implementation of three dimensional binary trees to efficiently find matches between points in 3D space. Two lists of points are compared and match indices as well as distances are given. K3Match can find either the nearest neighbour or all matches within a given search distance in 3D Cartesian space or on the surface of the 2D unit sphere in standard spherical or celestial coordinates.
Kadenza enables time-critical data analyses to be carried out using NASA's Kepler Space Telescope. It enables users to convert Kepler's raw data files into user-friendly Target Pixel Files upon downlink from the spacecraft. The primary motivation for this tool is to enable the microlensing, supernova, and exoplanet communities to create quicklook lightcurves for transient events which require rapid follow-up.
The fully parallelized and vectorized software package Kālī models time series data using various stochastic processes such as continuous-time ARMA (C-ARMA) processes and uses Bayesian Markov Chain Monte-Carlo (MCMC) for inferencing a stochastic light curve. Kālī is written in c++ with Python language bindings for ease of use. Kālī is named jointly after the Hindu goddess of time, change, and power and also as an acronym for KArma LIbrary.
Kalman models an inhomogeneous time series of measurements at different frequencies as noisy sampling from a finite mixture of Gaussian Ornstein-Uhlenbeck processes to try to reproduce the variability of the fluxes and of the spectral indices of the quasars used as calibrators in the Atacama Large Millimeter/Sub-millimeter Array (ALMA), assuming sensible parameters are provided to the model (obtained, for example, from maximum likelihood estimation). One routine in the Kalman Perl module calculates best forecast estimations based on a state space representation of the stochastic model using Kalman recursions, and another routine calculates the smoothed estimation (or interpolations) of the measurements and of the state space also using Kalman recursions. The code does not include optimization routines to calculate best fit parameters for the stochastic processes.
KAPPA comprising about 180 general-purpose commands for image processing, data visualization, and manipulation of the standard Starlink data format--the NDF. It works with Starlink's various specialized packages; in addition to the NDF, KAPPA can also process data in other formats by using the "on-the-fly" conversion scheme. Many commands can process data arrays of arbitrary dimension, and others work on both spectra and images. KAPPA operates from both the UNIX C-shell and the ICL command language. KAPPA uses the Starlink environment (ascl:1110.012).
Based on the freely available CHIANTI (ascl:9911.004) database and software, KAPPA synthesizes line and continuum spectra from the optically thin spectra that arise from collisionally dominated astrophysical plasmas that are the result of non-Maxwellian κ-distributions detected in the solar transition region and flares. Ionization and recombination rates together with the ionization equilibria are provided for a range of κ values. Distribution-averaged collision strengths for excitation are obtained by an approximate method for all transitions in all ions available within CHIANTI; KAPPA also offers tools for calculating synthetic line and continuum intensities.
The Kapteyn Package provides tools for the development of astronomical applications with Python. It handles spatial and spectral coordinates, WCS projections and transformations between different sky systems; spectral translations (e.g., between frequencies and velocities) and mixed coordinates are also supported. Kapteyn offers versatile tools for writing small and dedicated applications for the inspection of FITS headers, the extraction and display of (FITS) data, interactive inspection of this data (color editing) and for the creation of plots with world coordinate information. It includes utilities for use with matplotlib such as obtaining coordinate information from plots, interactively modifiable colormaps and timer events (module mplutil); tools for parsing and interpreting coordinate information entered by the user (module positions); a function to search for gaussian components in a profile (module profiles); and a class for non-linear least squares fitting (module kmpfit).
Karma is a toolkit for interprocess communications, authentication, encryption, graphics display, user interface and manipulating the Karma network data structure. It contains KarmaLib (the structured libraries and API) and a large number of modules (applications) to perform many standard tasks. A suite of visualisation tools are distributed with the library.
KAULAKYS calculates cross sections and rate coefficients for inelastic collisions between Rydberg atoms and hydrogen atoms according to the free electron model of Kaulakys (1986, 1991). It is written in IDL and requires the code MSWAVEF (ascl:1701.006) to calculate momentum-space wavefunctions. KAULAKYS can be easily adapted to collisions with perturbers other than hydrogen atoms by providing the appropriate scattering amplitudes.
kcorrect fits very restricted spectral energy distribution models to galaxy photometry or spectra in the restframe UV, optical and near-infrared. The main purpose of the fits are for calculating K-corrections. The templates used for the fits may also be interpreted physically, since they are based on the Bruzual-Charlot stellar evolution synthesis codes. Thus, for each fit galaxy kcorrect can provide an estimate of the stellar mass-to-light ratio.
The Kinematic Distance utilities (KDUtils) calculate kinematic distances and kinematic distance uncertainties. The package includes methods to calculate "traditional" kinematic distances as well as a Monte Carlo method to calculate kinematic distances and uncertainties.
KEPLER is a general purpose stellar evolution/explosion code that incorporates implicit hydrodynamics and a detailed treatment of nuclear burning processes. It has been used to study the complete evolution of massive and supermassive stars, all major classes of supernovae, hydrostatic and explosive nucleosynthesis, and x- and gamma-ray bursts on neutron stars and white dwarfs.
KeplerSolver solves Kepler's equation for arbitrary epoch and eccentricity, using continued fractions. It is written in C and its speed is nearly the same as the SWIFT routines, while achieving machine precision. It comes with a test program to demonstrate usage.
Keras is a high-level neural networks API written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It focuses on enabling fast experimentation.
KERN is a bi-annually released set of radio astronomical software packages. It should contain most of the standard tools that a radio astronomer needs to work with radio telescope data. The goal of KERN to is to save time and frustration in setting up of scientific pipelines, and to assist in achieving scientific reproducibility.
KERTAP computes the strong lensing effects of Kerr black holes, including the effects on polarization. The key ingredients of KERTAP are a graphic user interface, a backward ray-tracing algorithm, a polarization propagator dealing with gravitational Faraday rotation, and algorithms computing observables such as flux magnification and polarization angles.
ketu, written in Python, searches K2 light curves for evidence of exoplanets; the code simultaneously fits for systematic effects caused by small (few-pixel) drifts in the telescope pointing and other spacecraft issues and the transit signals of interest. Though more computationally expensive than standard search algorithms, it can be efficiently implemented and used to discover transit signals.
KINEMETRY, written in IDL, analyzes 2D maps of the moments of the line-of-sight velocity distribution (LOSVD). It generalizes the surface photometry to all moments of the LOSVD. It performs harmonic expansion of 2D maps of observed moments (surface brightness, velocity, velocity dispersion, h3, h4, etc.) along the best fitting ellipses (either fixed or free to change along the radii) to robustly quantify maps of the LOSVD moments, describe trends in structures, and detect morphological and kinematic sub-components.
The use of graphics processing units offers an attractive alternative to specialized hardware, like GRAPE. The Kirin library mimics the behavior of the GRAPE hardware and uses the GPU to execute the force calculations. It is compatible with the GRAPE6 library; existing code that uses the GRAPE6 library can be recompiled and relinked to use the GPU equivalents of the GRAPE6 functions. All functions in the GRAPE6 library have an equivalent GPU implementation. Kirin can be used for direct N-body simulations as well as for treecodes; it can be run with shared-time steps or with block time-steps and allows non-softened potentials. As Kirin makes use of CUDA, it works only on NVIDIA GPUs.
We present Kliko, a Docker based container specification for running one or multiple related compute jobs. The key concepts of Kliko is the encapsulation of data processing software into a container and the formalisation of the input, output and task parameters. Formalisation is realised by bundling a container with a Kliko file, which describes the IO and task parameters. This Kliko container can then be opened and run by a Kliko runner. The Kliko runner will parse the Kliko definition and gather the values for these parameters, for example by requesting user input or pre defined values in a script. Parameters can be various primitive types, for example float, int or the path to a file. This paper will also discuss the implementation of a support library named Kliko which can be used to create Kliko containers, parse Kliko definitions, chain Kliko containers in workflows using, for example, Luigi a workflow manager. The Kliko library can be used inside the container interact with the Kliko runner. Finally this paper will discuss two reference implementations based on Kliko: RODRIGUES, a web based Kliko container schedular and output visualiser specifically for astronomical data, and VerMeerKAT, a multi container workflow data reduction pipeline which is being used as a prototype pipeline for the commisioning of the MeerKAT radio telescope.
KMDWARFPARAM estimates the physical parameters of a star with mass M < 0.8 M_sun given one or more observational constraints. The code runs a Markov-Chain Monte Carlo procedure to estimate the parameter values and their uncertainties.
The kozai Python package evolves hierarchical triple systems in the secular approximation. As its name implies, the kozai package is useful for studying Kozai-Lidov oscillations. The kozai package can represent and evolve hierarchical triples using either the Delaunay orbital elements or the angular momentum and eccentricity vectors. kozai contains functions to calculate the period of Kozai-Lidov oscillations and the maximum eccentricity reached; it also contains a module to study octupole order effects by averaging over individual Kozai-Lidov oscillations.
kplr provides a lightweight Pythonic interface to the catalog of planet candidates (Kepler Objects of Interest [KOIs]) in the NASA Exoplanet Archive and the data stored in the Barbara A. Mikulski Archive for Space Telescopes (MAST). kplr automatically supports loading Kepler data using pyfits (ascl:1207.009) and supports two types of data: light curves and target pixel files.
Kranc turns a tensorial description of a time dependent partial differential equation into a module for the Cactus Computational Toolkit (ascl:1102.013). This Mathematica application takes a simple continuum description of a problem and generates highly efficient and portable code, and can be used both for rapid prototyping of evolution systems and for high performance supercomputing.
KROME, given a chemical network (in CSV format), automatically generates all the routines needed to solve the kinetics of the system modeled as a system of coupled Ordinary Differential Equations. It provides a large set of physical processes connected to chemistry, including photochemistry, cooling, heating, dust treatment, and reverse kinetics. KROME is flexible and can be used for a wide range of astrophysical simulations. The package contains a network for primordial chemistry, a small metal network appropriate for the modeling of low metallicities environments, a detailed network for the modeling of molecular clouds, and a network for planetary atmospheres as well as a framework for the modelling of the dust grain population.
KS Intergration solves for mutual photometric effects produced by planets and spots allowing for analysis of planetary occultations of spots and spots regions. It proceeds by identifying integrable and non integrable arcs on the objects profiles and analytically calculates the solution exploiting the power of Kelvin-Stokes theorem. It provides the solution up to the second degree of the limb darkening law.
KSTAT calculates the 2 and 3-point correlation functions in discreet point data. These include the two-point correlation function in 2 and 3-dimensions, the anisotripic 2PCF decomposed in either sigma-pi or Kazin's dist. mu projection. The 3-point correlation function can also work in anisotropic coordinates. The code is based on kd-tree structures and is parallelized using a mixture of MPI and OpenMP.
The routines in ktransit create and fit a transiting planet model. The underlying model is a Fortran implementation of the Mandel & Agol (2002) limb darkened transit model. The code calculates a full orbital model and eccentricity can be allowed to vary; radial velocity data can also be calculated via the model and included in the fit.
kungifu is a set of IDL software routines designed for the calibration and reduction of fiber-fed integral-field unit (IFU) astronomical spectroscopy. These routines can perform optimal extraction of IFU data and allow relative and absolute wavelength calibration to within a few hundredths of a pixel (for unbinned data) across 1200-2000 fibers. kungifu does nearly Poisson-limited sky subtraction, even in the I band, and can rebin in wavelength. The Princeton IDLUTILS and IDLSPEC2D packages must be installed for kungifu to run.
L-PICOLA generates and evolves a set of initial conditions into a dark matter field and can include primordial non-Gaussianity in the simulation and simulate the past lightcone at run-time, with optional replication of the simulation volume. It is a fast, distributed-memory, planar-parallel code. L-PICOLA is extremely useful for both current and next generation large-scale structure surveys.
Conventional algorithms for rejecting cosmic rays in single CCD exposures rely on the contrast between cosmic rays and their surroundings and may produce erroneous results if the point-spread function is smaller than the largest cosmic rays. This code uses a robust algorithm for cosmic-ray rejection, based on a variation of Laplacian edge detection. The algorithm identifies cosmic rays of arbitrary shapes and sizes by the sharpness of their edges and reliably discriminates between poorly sampled point sources and cosmic rays. Examples of its performance are given for spectroscopic and imaging data, including Hubble Space Telescope Wide Field Planetary Camera 2 images, in the code paper.
LACEwING (LocAting Constituent mEmbers In Nearby Groups) uses the kinematics (positions and motions) of stars to determine if they are members of one of 10 nearby young moving groups or 4 nearby open clusters within 100 parsecs. It is written for Python 2.7 and depends upon Numpy, Scipy, and Astropy (ascl:1304.002) modules. LACEwING can be used as a stand-alone code or as a module in other code. Additional python programs are present in the repository for the purpose of recalibrating the code and producing other analyses, including a traceback analysis.
LAMBDAR measures galaxy fluxes from an arbitrary FITS image, covering an arbitrary photometric wave-band, when provided all parameters needed to construct galactic apertures at the required locations for multi-band matched aperture galactic photometry. Through sophisticated matched aperture photometry, the package develops robust Spectral Energy Distributions (SEDs) and accurately establishes the physical properties of galactic objects. LAMBDAR was based on a package detailed in Bourne et al. (2012) that determined galactic fluxes in low resolution Herschel images.
LAMDA provides users of radiative transfer codes with the basic atomic and molecular data needed for the excitation calculation. Line data of a number of astrophysically interesting species are summarized, including energy levels, statistical weights, Einstein A-coefficients and collisional rate coefficients. Available collisional data from quantum chemical calculations and experiments are in some cases extrapolated to higher energies. Currently the database contains atomic data for 3 species and molecular data for 28 different species. In addition, several isotopomers and deuterated versions are available. This database should form an important tool in analyzing observations from current and future infrared and (sub)millimetre telescopes. Databases such as these rely heavily on the efforts by the chemical physics community to provide the relevant atomic and molecular data. Further efforts in this direction are strongly encouraged so that the current extrapolations of collisional rate coefficients can be replaced by actual calculations in future releases.
RADEX, a computer program for performing statistical equilibrium calculations is made publicly available as part of the data base.
LANL* calculates the magnetic drift invariant L*, used for modeling radiation belt dynamics and other space weather applications, six orders of magnitude (~ one million times) faster than convectional approaches that require global numerical field lines tracing and integration. It is based on a modern machine learning technique (feed-forward artificial neural network) by supervising a large data pool obtained from the IRBEM library, which is the traditional source for numerically calculating the L* values. The pool consists of about 100,000 samples randomly distributed within the magnetosphere (r: [1.03, 11.5] Re) and within a whole solar cycle from 1/1/1994 to 1/1/2005. There are seven LANL* models, each corresponding to its underlying magnetic field configuration that is used to create the data sample pool. This model has applications to real-time radiation belt forecasting, analysis of data sets involving tens of satellite-years of observations, and other problems in space weather.
Larch is an open-source library and toolkit written in Python for processing and analyzing X-ray spectroscopic data. The primary emphasis is on X-ray spectroscopic and scattering data collected at modern synchrotron sources. Larch provides a wide selection of general-purpose processing, analysis, and visualization tools for processing X-ray data; its related target application areas include X-ray absorption fine structure (XAFS), micro-X-ray fluorescence (XRF) maps, quantitative X-ray fluorescence, X-ray absorption near edge spectroscopy (XANES), and X-ray standing waves and surface scattering. Larch provides a complete set of XAFS Analysis tools and has support for visualizing and analyzing XRF maps and spectra, and additional tools for X-ray spectral analysis, data handling, and general-purpose data modeling.
Lare3d is a Lagrangian-remap code for solving the non-linear MHD equations in three spatial dimensions.
LASR removes stellar variability in the light curves of δ-Scuti and similar stars. It subtracts oscillations from a time series by minimizing their statistical significance in frequency space.
LATTICEEASY creates lattice simulations of the evolution of interacting scalar fields in an expanding universe. The program can do runs with different parameters and new models can be easily introduced for evaluation. Simulations can be done in one, two, or three dimensions by resetting a single variable. Mathematica notebooks for plotting the output and a range of models are also available for download; a parallel processing version of LATTICEEASY called CLUSTEREASY (ascl:1911.016) is also available.
The Lattimer-Swesty Equation of State code is rapid enough to use directly in hydrodynamical simulations such as stellar collapse calculations. It contains an adjustable nuclear force that accurately models both potential and mean-field interactions and allows for the input of various nuclear parameters, including the bulk incompressibility parameter, the bulk and surface symmetry energies, the symmetric matter surface tension, and the nucleon effective masses. This permits parametric studies of the equation of state in astrophysical situations. The equation of state is modeled after the Lattimer, Lamb, Pethick, and Ravenhall (LLPR) compressible liquid drop model for nuclei, and includes the effects of interactions and degeneracy of the nucleon outside nuclei.
LBLRTM (Line-By-Line Radiative Transfer Model) is an accurate line-by-line model that is efficient and highly flexible. LBLRTM attributes provide spectral radiance calculations with accuracies consistent with the measurements against which they are validated and with computational times that greatly facilitate the application of the line-by-line approach to current radiative transfer applications. LBLRTM has been extensively validated against atmospheric radiance spectra from the ultra-violet to the sub-millimeter.
LBLRTM's heritage is in FASCODE [Clough et al., 1981, 1992].
Light Curves Classifier uses data mining and machine learning to obtain and classify desired objects. This task can be accomplished by attributes of light curves or any time series, including shapes, histograms, or variograms, or by other available information about the inspected objects, such as color indices, temperatures, and abundances. After specifying features which describe the objects to be searched, the software trains on a given training sample, and can then be used for unsupervised clustering for visualizing the natural separation of the sample. The package can be also used for automatic tuning parameters of used methods (for example, number of hidden neurons or binning ratio).
Trained classifiers can be used for filtering outputs from astronomical databases or data stored locally. The Light Curve Classifier can also be used for simple downloading of light curves and all available information of queried stars. It natively can connect to OgleII, OgleIII, ASAS, CoRoT, Kepler, Catalina and MACHO, and new connectors or descriptors can be implemented. In addition to direct usage of the package and command line UI, the program can be used through a web interface. Users can create jobs for ”training” methods on given objects, querying databases and filtering outputs by trained filters. Preimplemented descriptors, classifier and connectors can be picked by simple clicks and their parameters can be tuned by giving ranges of these values. All combinations are then calculated and the best one is used for creating the filter. Natural separation of the data can be visualized by unsupervised clustering.
lcps searches for transit-like features (i.e., dips) in photometric data. Its main purpose is to restrict large sets of light curves to a number of files that show interesting behavior, such as drops in flux. While lcps is adaptable to any format of time series, its I/O module is designed specifically for photometry of the Kepler spacecraft. It extracts the pre-conditioned PDCSAP data from light curves files created by the standard Kepler pipeline. It can also handle csv-formatted ascii files. lcps uses a sliding window technique to compare a section of flux time series with its surroundings. A dip is detected if the flux within the window is lower than a threshold fraction of the surrounding fluxes.
LDC3 samples physically permissible limb darkening coefficients for the Sing et al. (2009) three-parameter law. It defines the physically permissible intensity profile as being everywhere-positive, monotonically decreasing from center to limb and having a curl at the limb. The approximate sampling method is analytic and thus very fast, reproducing physically permissible samples in 97.3% of random draws (high validity) and encompassing 94.4% of the physically permissible parameter volume (high completeness).
Least Asymmetry finds the center of a distribution of light in an image using the least asymmetry method; the code also contains center of light and fitting a Gaussian routines. All functions in Least Asymmetry are designed to take optional weights.
LECTOR is a Fortran 77 code that measures line-strengths in one dimensional ascii spectra. The code returns the values of the Lick indices as well as those of Vazdekis & Arimoto 1999, Vazdekis et al. 2001, Rose 1994, Jones & Worthey 1995 and Cenarro et al. 2001. The code measures as many indices as you wish if the limits of two pseudocontinua (at each side of the feature) and the feature itself (i.e. Lick-style index definition) are provided. The Lick-style indices could be either expressed in pseudo-equivalent widths or in magnitudes. If requested the program provides index error estimates on the basis of photon statistics.
LEMON is a differential-photometry pipeline, written in Python, that determines the changes in the brightness of astronomical objects over time and compiles their measurements into light curves. This code makes it possible to completely reduce thousands of FITS images of time series in a matter of only a few hours, requiring minimal user interaction.
The LensCNN (Convolutional Neural Network) identifies images containing gravitational lensing systems after being trained and tested on simulated images, recovering most systems that are identifiable by eye.
Lensed performs forward parametric modelling of strong lenses. Using a provided model, Lensed renders the expected image of the lensing event for a large number of parameter settings, thereby exploring the space of possible realizations of the observation. It compares the expectation to the observed image by calculating the likelihood that the observation was indeed produced by the assumed model, thus reconstructing the probability distribution over the parameter space of the model. Written in C, the code uses a massively parallel ray-tracing kernel to perform the necessary calculations on a graphics processing unit (GPU), making the precise rendering of the background lensed sources fast and allowing the simultaneous optimization of tens of parameters for the selected model.
LensEnt2 is a maximum entropy reconstructor of weak lensing mass maps. The method takes each galaxy shape as an independent estimator of the reduced shear field and incorporates an intrinsic smoothness, determined by Bayesian methods, into the reconstruction. The uncertainties from both the intrinsic distribution of galaxy shapes and galaxy shape estimation are carried through to the final mass reconstruction, and the mass within arbitrarily shaped apertures are calculated with corresponding uncertainties. The input is a galaxy ellipticity catalog with each measured galaxy shape treated as a noisy tracer of the reduced shear field, which is inferred on a fine pixel grid assuming positivity, and smoothness on scales of w arcsec where w is an input parameter. The ICF width w can be chosen by computing the evidence for it.
Given a model for the Galaxy, this program computes the microlensing rate in any direction. Program features include the ability to include the brightness of the lens and to compute the probability of lens detection at any level of lensing amplification. The program limits itself to lensing by single stars of single sources. The program is currently setup to accept input from the Galactic models of Bahcall and Soniera (1982, 1986).
There are three files needed for LENSKY, the Fortran file lensky.for and two input files: galmod.dsk (15 Megs) and galmod.sph (22 Megs). The zip file available below contains all three files. The program generates output to the file lensky.out. The program is pretty self-explanatory past that.
LensPerfect is a new approach to the massmap reconstruction of strong gravitational lenses. Conventional methods iterate over possible lens models which reproduce the observed multiple image positions well but not exactly. LensPerfect only produces solutions which fit all of the data exactly. Magnifications and shears of the multiple images can also be perfectly constrained to match observations.
Modelling of the weak lensing of the CMB will be crucial to obtain correct cosmological parameter constraints from forthcoming precision CMB anisotropy observations. The lensing affects the power spectrum as well as inducing non-Gaussianities. We discuss the simulation of full sky CMB maps in the weak lensing approximation and describe a fast numerical code. The series expansion in the deflection angle cannot be used to simulate accurate CMB maps, so a pixel remapping must be used. For parameter estimation accounting for the change in the power spectrum but assuming Gaussianity is sufficient to obtain accurate results up to Planck sensitivity using current tools. A fuller analysis may be required to obtain accurate error estimates and for more sensitive observations. We demonstrate a simple full sky simulation and subsequent parameter estimation at Planck-like sensitivity.
LensPop simulates observations of the galaxy-galaxy strong lensing population in the Dark Energy Survey (DES), the Large Synoptic Survey Telescope (LSST), and Euclid surveys.
LensQuEst forecasts the signal-to-noise of CMB lensing estimators (standard, shear-only, magnification-only), generates mock maps, lenses them, and applies various lensing estimators to them. It can manipulate flat sky maps in various ways, including FFT, filtering, power spectrum, generating Gaussian random field, and applying lensing to a map, and evaluate these estimators on flat sky maps.
We describe a procedure for modelling strong lensing galaxy clusters with parametric methods, and to rank models quantitatively using the Bayesian evidence. We use a publicly available Markov chain Monte-Carlo (MCMC) sampler ('Bayesys'), allowing us to avoid local minima in the likelihood functions. To illustrate the power of the MCMC technique, we simulate three clusters of galaxies, each composed of a cluster-scale halo and a set of perturbing galaxy-scale subhalos. We ray-trace three light beams through each model to produce a catalogue of multiple images, and then use the MCMC sampler to recover the model parameters in the three different lensing configurations. We find that, for typical Hubble Space Telescope (HST)-quality imaging data, the total mass in the Einstein radius is recovered with ~1-5% error according to the considered lensing configuration. However, we find that the mass of the galaxies is strongly degenerated with the cluster mass when no multiple images appear in the cluster centre. The mass of the galaxies is generally recovered with a 20% error, largely due to the poorly constrained cut-off radius. Finally, we describe how to rank models quantitatively using the Bayesian evidence. We confirm the ability of strong lensing to constrain the mass profile in the central region of galaxy clusters in this way. Ultimately, such a method applied to strong lensing clusters with a very large number of multiple images may provide unique geometrical constraints on cosmology.
LensTools implements a wide range of routines frequently used in Weak Gravitational Lensing, including tools for image analysis, statistical processing and numerical theory predictions. The package offers many useful features, including complete flexibility and easy customization of input/output formats; efficient measurements of power spectrum, PDF, Minkowski functionals and peak counts of convergence maps; survey masks; artificial noise generation engines; easy to compute parameter statistical inferences; ray tracing simulations; and many others. It requires standard numpy and scipy, and depending on tools used, may require Astropy (ascl:1304.002), emcee (ascl:1303.002), matplotlib, and mpi4py.
Lenstronomy is a multi-purpose open-source gravitational lens modeling python package. Lenstronomy reconstructs the lens mass and surface brightness distributions of strong lensing systems using forward modelling and supports a wide range of analytic lens and light models in arbitrary combination. The software is also able to reconstruct complex extended sources as well as point sources. Lenstronomy is flexible and numerically accurate, with a clear user interface that could be deployed across different platforms. Lenstronomy has been used to derive constraints on dark matter properties in strong lenses, measure the expansion history of the universe with time-delay cosmography, measure cosmic shear with Einstein rings, and decompose quasar and host galaxy light.
Lensview models resolved gravitational lens systems based on LensMEM but using the Skilling & Bryan MEM algorithm. Though its primary purpose is to find statistically acceptable lens models for lensed images and to reconstruct the surface brightness profile of the source, LENSVIEW can also be used for more simple tasks such as projecting a given source through a lens model to generate a “true” image by conserving surface brightness. The user can specify complicated lens models based on one or more components, such as softened isothermal ellipsoids, point masses, exponential discs, and external shears; LENSVIEW generates a best-fitting source matching the observed data for each specific combination of model parameters.
LEO-Py uses a novel technique to compute the likelihood function for data sets with uncertain, missing, censored, and correlated values. It uses Gaussian copulas to decouple the correlation structure of variables and their marginal distributions to compute likelihood functions, thus mitigating inconsistent parameter estimates and accounting for non-normal distributions in variables of interest or their errors.
LePHARE is a set of Fortran commands to compute photometric redshifts and to perform SED fitting. The latest version includes new features with FIR fitting and a more complete treatment of physical parameters and uncertainties based on PÉGASE and Bruzual & Charlot population synthesis models. The program is based on a simple chi2 fitting method between the theoretical and observed photometric catalogue. A simulation program is also available in order to generate realistic multi-colour catalogues taking into account observational effects.
LExTeS (Link Extraction and Testing Suite) extracts hyperlinks from PDF documents, tests the extracted links to see which are broken, and tabulates the results. Though written to support a particular set of PDF documents, the dataset and scripts can be edited for use on other documents.
LFlGRB models the luminosity function (LF) of long Gamma Ray Bursts (lGRBs) by using a sample of Swift and Fermi lGRBs to re-derive the parameters of the Yonetoku correlation and self-consistently estimate pseudo-redshifts of all the bursts with unknown redshifts. The GRB formation rate is modeled as the product of the cosmic star formation rate and a GRB formation efficiency for a given stellar mass.
LFsGRB models the luminosity function (LF) of short Gamma Ray Bursts (sGRBs) by using the available catalog data of all short GRBs (sGRBs) detected till 2017 October, estimating the luminosities via pseudo-redshifts obtained from the Yonetoku correlation, and then assuming a standard delay distribution between the cosmic star formation rate and the production rate of their progenitors. The data are fit well both by exponential cutoff powerlaw and broken powerlaw models. Using the derived parameters of these models along with conservative values in the jet opening angles seen from afterglow observations, the true rate of short GRBs is derived. Assuming a short GRB is produced from each binary neutron star merger (BNSM), the rate of gravitational wave (GW) detections from these mergers are derived for the past, present and future configurations of the GW detector networks.
LGMCA (Local-Generalized Morphological Component Analysis) is an extension to GMCA (ascl:1710.015). Similarly to GMCA, it is a Blind Source Separation method which enforces sparsity. The novel aspect of LGMCA, however, is that the mixing matrix changes across pixels allowing LGMCA to deal with emissions sources which vary spatially. These IDL scripts compute the CMB map from WMAP and Planck data; running LGMCA on the WMAP9 temperature products requires the main script and a selection of mandatory files, algorithm parameters and map parameters.
LgrbWorldModel is written in Fortran 90 and attempts to model the population distribution of the Long-duration class of Gamma-Ray Bursts (LGRBs) as detected by the NASA's now-defunct Burst And Transient Source Experiment (BATSE) onboard the Compton Gamma Ray Observatory (CGRO). It is assumed that the population distribution of LGRBs is well fit by a multivariate log-normal distribution. The best-fit parameters of the distribution are then found by maximizing the likelihood of the observed data by BATSE detectors via a native built-in Adaptive Metropolis-Hastings Markov-Chain Monte Carlo (AMH-MCMC) Sampler.
The Long Wavelength Spectrometer (LWS) was one of two complementary spectrometers on the Infrared Space Observatory (ISO). LIA (LWS Interactive Analysis) is used for processing data from the LWS. It provides access to the different processing steps, including visualization of intermediate products and interactive manipulation of the data at each stage.
Libimf provides a collection of programming functions based on the general IMF-algorithm by Pflamm-Altenburg & Kroupa (2006).
libnova is a general purpose, double precision, celestial mechanics, astrometry and astrodynamics library. Among many other calculations, it can calculate aberration, apparent position, proper motion, planetary positions, orbit velocities and lengths, angular separation of bodies, and hyperbolic motion of bodies.
Libpolycomp compresses and decompresses one-dimensional streams of numbers by means of several algorithms. It is well-suited for time-ordered data acquired by astronomical instruments or simulations. One of the algorithms, called "polynomial compression", combines two widely-used ideas (namely, polynomial approximation and filtering of Fourier series) to achieve substantial compression ratios for datasets characterized by smoothness and lack of noise. Notable examples are the ephemerides of astronomical objects and the pointing information of astronomical telescopes. Other algorithms implemented in this C library are well known and already widely used, e.g., RLE, quantization, deflate (via libz) and Burrows-Wheeler transform (via libbzip2). Libpolycomp can compress the timelines acquired by the Planck/LFI instrument with an overall compression ratio of ~9, while other widely known programs (gzip, bzip2) reach compression ratios less than 1.5.
libprofit is a C++ library for image creation based on different luminosity profiles. It offers fast and accurate two-dimensional integration for a useful number of profiles, including Sersic, Core-Sersic, broken-exponential, Ferrer, Moffat, empirical King, point-source and sky, with a simple mechanism for adding new profiles. libprofit provides a utility to read the model and profile parameters from the command-line and generate the corresponding image. It can output the resulting image as text values, a binary stream, or as a simple FITS file. It also provides a shared library exposing an API that can be used by any third-party application. R and Python interfaces are available: ProFit (ascl:1612.004) and PyProfit (ascl:1612.005).
Libpsht (or "library for Performing Spherical Harmonic Transforms") is a collection of algorithms for efficient conversion between spatial-domain and spectral-domain representations of data defined on the sphere. The package supports transforms of scalars as well as spin-1 and spin-2 quantities, and can be used for a wide range of pixelisations (including HEALPix, GLESP and ECP). It will take advantage of hardware features like multiple processor cores and floating-point vector operations, if available. Even without this additional acceleration, the employed algorithms are among the most efficient (in terms of CPU time as well as memory consumption) currently being used in the astronomical community.
The library is written in strictly standard-conforming C90, ensuring portability to many different hard- and software platforms, and allowing straightforward integration with codes written in various programming languages like C, C++, Fortran, Python etc.
Libpsht is distributed under the terms of the GNU General Public License (GPL) version 2.
Development on this project has ended; its successor is libsharp (ascl:1402.033).
Libsharp is a collection of algorithms for efficient conversion between maps on the sphere and their spherical harmonic coefficients. It supports a wide range of pixelisations (including HEALPix, GLESP, and ECP). This library is a successor of libpsht (ascl:1010.020); it adds MPI support for distributed memory systems and SHTs of fields with arbitrary spin, and also supports new developments in CPU instruction sets like the Advanced Vector Extensions (AVX) or fused multiply-accumulate (FMA) instructions. libsharp is written in portable C99; it provides an interface accessible to other programming languages such as C++, Fortran, and Python.
libstempo uses the Tempo2 library (ascl:1210.015) to load a pulsar's tim/par files, providing Python access to the TOAs, the residuals, the timing-model parameters, the fit procedure, and more.
Lightcone works with simulated galaxy data stored in a relational database to rearrange the data in a shape of a light-cone; simulated galaxy data is expected to be in a box volume. The light-cone constructing script works with output from the SAGE semi-analytic model (ascl:1601.006), but will work with any other model that has galaxy positions (and other properties) saved per snapshots of the simulation volume distributed in time. The database configuration file is set up for PostgreSQL RDBMS, but can be modified for use with any other SQL database.
LightcurveMC is a versatile and easily extended simulation suite for testing the performance of time series analysis tools under controlled conditions. It is designed to be highly modular, allowing new lightcurve types or new analysis tools to be introduced without excessive development overhead. The statistical tools are completely agnostic to how the lightcurve data is generated, and the lightcurve generators are completely agnostic to how the data will be analyzed. The use of fixed random seeds throughout guarantees that the program generates consistent results from run to run.
LightcurveMC can generate periodic light curves having a variety of shapes and stochastic light curves having a variety of correlation properties. It features two error models (Gaussian measurement and signal injection using a randomized sample of base light curves), testing of C1 shape statistic, periodograms, ΔmΔt plots, autocorrelation function plots, peak-finding plots, and Gaussian process regression. The code is written in C++ and R.
Lightkurve analyzes astronomical flux time series data, in particular the pixels and light curves obtained by NASA’s Kepler, K2, and TESS exoplanet missions. This community-developed Python package is designed to be user friendly to lower the barrier for students, astronomers, and citizen scientists interested in analyzing data from these missions. Lightkurve provides easy tools to download, inspect, and analyze time series data and its documentation is supported by a large syllabus of tutorials.
Lightning is a spectral energy distribution (SED) fitting procedure that quickly and reliably recovers star formation history (SFH) and extinction parameters. The SFH is modeled as discrete steps in time. The code consists of a fully vectorized inversion algorithm to determine SFH step intensities and combines this with a grid-based approach to determine three extinction parameters.
Limb-darkening generates limb-darkening coefficients from ATLAS and PHOENIX model atmospheres using arbitrary response functions. The code uses PyFITS (ascl:1207.009) and has several other dependencies, and produces a folder of results with descriptions of the columns contained in each file.
LIME solves the molecular and atomic excitation and radiation transfer problem in a molecular gas and predicting emergent spectra. The code works in arbitrary three dimensional geometry using unstructured Delaunay latices for the transport of photons. Various physical models can be used as input, ranging from analytical descriptions over tabulated models to SPH simulations. To generate the Delaunay grid we sample the input model randomly, but weigh the sample probability with the molecular density and other parameters, and thereby we obtain an average grid point separation that scales with the local opacity. Slow convergence of opaque models becomes traceable; when convergence between the level populations, the radiation field, and the point separation has been obtained, the grid is ray-traced to produced images that can readily be compared to observations. LIME is particularly well suited for modeling of ALMA data because of the high dynamic range in scales that can be resolved using this type of grid, and can furthermore deal with overlapping lines of multiple molecular and atomic species.
LIMEPY solves distribution function (DF) based lowered isothermal models. It solves Poisson's equation used on input parameters and offers fast solutions for isotropic/anisotropic, single/multi-mass models, normalized DF values, density and velocity moments, projected properties, and generates discrete samples.
LineProf implements a series of line-profile analysis indicators and evaluates its correlation with RV data. It receives as input a list of Cross-Correlation Functions and an optional list of associated RV. It evaluates the line-profile according to the indicators and compares it with the computed RV if no associated RV is provided, or with the provided RV otherwise.
LIRA (LInear Regression in Astronomy) performs Bayesian linear regression that accounts for heteroscedastic errors in both the independent and the dependent variables, intrinsic scatters (in both variables), time evolution of slopes, normalization and scatters, Malmquist and Eddington bias, and break of linearity. The posterior distribution of the regression parameters is sampled with a Gibbs method exploiting the JAGS (ascl:1209.002) library.
LIRA (Low-counts Image Reconstruction and Analysis) deconvolves any unknown sky components, provides a fully Poisson 'goodness-of-fit' for any best-fit model, and quantifies uncertainties on the existence and shape of unknown sky. It does this without resorting to χ2 or rebinning, which can lose high-resolution information. It is written in R and requires the FITSio package.
LISACode is a simulator of the LISA mission. Its ambition is to achieve a new degree of sophistication allowing to map, as closely as possible, the impact of the different subsystems on the measurements. Its also a useful tool for generating realistic data including several kind of sources (Massive Black Hole binaries, EMRIs, cosmic string cusp, stochastic background, etc) and for preparing their analysis. It’s fully integrated to the Mock LISA Data Challenge. LISACode is not a detailed simulator at the engineering level but rather a tool whose purpose is to bridge the gap between the basic principles of LISA and a future, sophisticated end-to-end simulator.
LiveData is a multibeam single-dish data reduction system for bandpass calibration and gridding. It is used for processing Parkes multibeam and Mopra data.
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