Results 1101-1150 of 2121 (2090 ASCL, 31 submitted)
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.
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].
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.
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.
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.
Lare3d is a Lagrangian-remap code for solving the non-linear MHD equations in three spatial dimensions.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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).
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.
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.
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.
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.
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).
K2PS is an Oxford K2 planet search pipeline. Written in Python, it searches for transit-like signals from the k2sc-detrended light curves.
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).
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.
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.
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.
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.
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.
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