Results 1201-1250 of 2331 (2295 ASCL, 36 submitted)
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
kombine is an ensemble sampler built for efficiently exploring multimodal distributions. By using estimates of ensemble’s instantaneous distribution as a proposal, it achieves very fast burnin, followed by sampling with very short autocorrelation times.
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.
KLLR (Kernel Localized Linear Regression) generates estimates of conditional statistics in terms of the local slope, normalization, and covariance. This method provides a more nuanced description of population statistics appropriate for very large samples with non-linear trends. The code uses a bootstrap re-sampling technique to estimate the uncertainties and also provides tools to seamlessly generate visualizations of the model parameters.
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.
The KinMS (KINematic Molecular Simulation) package simulates observations of arbitrary molecular/atomic cold gas distributions from interferometers and line observations from integral field units. This modeling tool is optimized for situations where one has analytic forms for e.g. the rotation curve and/or surface brightness profiles (and may want to fit the parameters of these parametric models). It can, however, also be used as a tilted-ring modelling code. The routines are flexible and have been used in various different applications, including investigating the kinematics of molecular gas in early-type galaxies and determining supermassive black-hole masses from CO interferometric observations. They are also useful for creating mock observations from hydrodynamic simulations, and input data-cubes for further simulation in, for example, CASA's (ascl:1107.013) sim_observe tool. Interactive Data Language (IDL) and Python versions of the code are available.
Kinesis fits the internal kinematics of a star cluster with astrometry and (incomplete) radial velocity data of its members. In the most general model, the stars can be a mixture of background (contamination) and the cluster, for which the (3,3) velocity dispersion matrix and velocity gradient (i.e., dv_x/dx and dv_y/dx) are included. There are also simpler versions of the most general model and utilities to generate mock clusters and mock observations.
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.
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