Results 701-750 of 1772 (1747 ASCL, 25 submitted)
The great majority of X-ray measurements of cluster masses in the literature assume parametrized functional forms for the radial distribution of two independent cluster thermodynamic properties, such as electron density and temperature, to model the X-ray surface brightness. These radial profiles (e.g. β-model) have an amplitude normalization parameter and two or more shape parameters. BAYES-X uses a cluster model to parametrize the radial X-ray surface brightness profile and explore the constraints on both model parameters and physical parameters. Bayes-X is programmed in Fortran and uses MultiNest (ascl:1109.006) as the Bayesian inference engine.
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.
pyMCZ calculates metallicity according to a number of strong line metallicity diagnostics from spectroscopy line measurements and obtain uncertainties from the line flux errors in a Monte Carlo framework. Given line flux measurements and their uncertainties, pyMCZ produces synthetic distributions for the oxygen abundance in up to 13 metallicity scales simultaneously, as well as for E(B-V), and estimates their median values and their 68% confidence regions. The code can output the full MC distributions and their kernel density estimates.
PyTransit implements optimized versions of the Giménez and Mandel & Agol transit models for exoplanet transit light-curves. The two models are implemented natively in Fortran with OpenMP parallelization, and are accessed by an object-oriented python interface. PyTransit facilitates the analysis of photometric time series of exoplanet transits consisting of hundreds of thousands of data points, and of multipassband transit light curves from spectrophotometric observations. It offers efficient model evaluation for multicolour observations and transmission spectroscopy, built-in supersampling to account for extended exposure times, and routines to calculate the projected planet-to-star distance for circular and eccentric orbits, transit durations, and more.
The SNooPy package (also known as SNpy), written in Python, contains tools for the analysis of TypeIa supernovae. It offers interactive plotting of light-curve data and models (and spectra), computation of reddening laws and K-corrections, LM non-linear least-squares fitting of light-curve data, and various types of spline fitting, including Diercx and tension. The package also includes a SNIa lightcurve template generator in the CSP passbands, estimates of Milky-Way Extinction, and a module for dealing with filters and spectra.
Snoopy is a spectral 3D code that solves the MHD and Boussinesq equations, such as compressibility, particles, and Braginskii viscosity, and several other physical effects. It's useful for turbulence study involving shear and rotation. Snoopy requires the FFTW library (ascl:1201.015), and can run on parallel machine using MPI OpenMP or both at the same time.
rvfit, developed in IDL 7.0, fits non-precessing keplerian radial velocity (RV) curves for double-line and single-line binary stars or exoplanets. It fits a simple keplerian model to the observed RV and computes the seven parameters (six for a single-line system) from the model. Some parameters can be fixed beforehand if they are known, for instance, if photometric observations are available. The fit is done using an Adaptive Simulated Annealing algorithm optimized for this specific task. Simulated Annealing methods are powerful heuristic algorithms to minimize functions in multiparametric spaces.
TFIT measures galaxy photometry using prior knowledge of sources in a deep, high‐resolution image (HRI) to improve photometric measurements of objects in a corresponding low‐resolution image (LRI) of the same field, usually at a different wavelength. For background‐limited data, this technique produces optimally weighted photometry that maximizes signal‐to‐noise ratio (S/N). For objects not significantly detected in the low‐resolution image, it provides useful and quantitative information for setting upper limits.
This code is no longer updated and has been superseded by T-PHOT (ascl:1609.001).
POKER (P Of K EstimatoR) estimates the angular power spectrum of a 2D map or the cross-power spectrum of two 2D maps in the flat sky limit approximation in a realistic data context: steep power spectrum, non periodic boundary conditions, arbitrary pixel resolution, non trivial masks and observation patch geometry.
HALOGEN generates approximate synthetic halo catalogs. Written in C, it decomposes the problem of generating cosmological tracer distributions (eg. halos) into four steps: generating an approximate density field, generating the required number of tracers from a CDF over mass, placing the tracers on field particles according to a bias scheme dependent on local density, and assigning velocities to the tracers based on velocities of local particles. It also implements a default set of four models for these steps. HALOGEN uses 2LPTic (ascl:1201.005) and CUTE (ascl:1505.016); the software is flexible and can be adapted to varying cosmologies and simulation specifications.
CUTE (Correlation Utilities and Two-point Estimation) extracts any two-point statistic from enormous datasets with hundreds of millions of objects, such as large galaxy surveys. The computational time grows with the square of the number of objects to be correlated; technology provides multiple means to massively parallelize this problem and CUTE is specifically designed for these kind of calculations. Two implementations are provided: one for execution on shared-memory machines using OpenMP and one that runs on graphical processing units (GPUs) using CUDA.
2dfdr is an automatic data reduction pipeline dedicated to reducing multi-fibre spectroscopy data, with current implementations for AAOmega (fed by the 2dF, KOALA-IFU, SAMI Multi-IFU or older SPIRAL front-ends), HERMES, 2dF (spectrograph), 6dF, and FMOS. A graphical user interface is provided to control data reduction and allow inspection of the reduced spectra.
FCLC (Featureless Classification of Light Curves) software describes the static behavior of a light curve in a probabilistic way. Individual data points are converted to densities and consequently probability density are compared instead of features. This gives rise to an independent classification which can corroborate the usefulness of the selected features.
Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of mock data and comparison between observed and synthetic catalogs. cosmoabc is a Python Approximate Bayesian Computation (ABC) sampler featuring a Population Monte Carlo variation of the original ABC algorithm, which uses an adaptive importance sampling scheme. The code can be coupled to an external simulator to allow incorporation of arbitrary distance and prior functions. When coupled with the numcosmo library, it has been used to estimate posterior probability distributions over cosmological parameters based on measurements of galaxy clusters number counts without computing the likelihood function.
LSSGALPY provides visualization tools to compare the 3D positions of a sample (or samples) of isolated systems with respect to the locations of the large-scale structures galaxies in their local and/or large scale environments. The interactive tools use different projections in the 3D space (right ascension, declination, and redshift) to study the relation of the galaxies with the LSS. The tools permit visualization of the locations of the galaxies for different values of redshifts and redshift ranges; the relationship of isolated galaxies, isolated pairs, and isolated triplets to the galaxies in the LSS can be visualized for different values of the declinations and declination ranges.
missForest imputes missing values particularly in the case of mixed-type data. It uses a random forest trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation and can be run in parallel to save computation time. missForest has been used to, among other things, impute variable star colors in an All-Sky Automated Survey (ASAS) dataset of variable stars with no NOMAD match.
COBS (COnstrained B-Splines), written in R, creates constrained regression smoothing splines via linear programming and sparse matrices. The method has two important features: the number and location of knots for the spline fit are established using the likelihood-based Akaike Information Criterion (rather than a heuristic procedure); and fits can be made for quantiles (e.g. 25% and 75% as well as the usual 50%) in the response variable, which is valuable when the scatter is asymmetrical or non-Gaussian. This code is useful for, for example, estimating cluster ages when there is a wide spread in stellar ages at a chosen absorption, as a standard regression line does not give an effective measure of this relationship.
stellaR accesses and manipulates publicly available stellar evolutionary tracks and isochrones from the Pisa low-mass database. It retrieves and plots the required calculations from CDS, constructs by interpolation tracks or isochrones of compositions different to the ones available in the database, constructs isochrones for age not included in the database, and extracts relevant evolutionary points from tracks or isochrones.
SCEPtER (Stellar CharactEristics Pisa Estimation gRid) estimates the stellar mass and radius given a set of observable quantities; the results are obtained by adopting a maximum likelihood technique over a grid of pre-computed stellar models. The code is quite flexible since different observables can be used, depending on their availability, as well as different grids of models.
Starfish is a set of tools used for spectroscopic inference. It robustly determines stellar parameters using high resolution spectral models and uses Markov Chain Monte Carlo (MCMC) to explore the full posterior probability distribution of the stellar parameters. Additional potential applications include other types of spectra, such as unresolved stellar clusters or supernovae spectra.
Written in FORTRAN, Athena3D, based on Athena (ascl:1010.014), is an implementation of a flux-conservative Godunov-type algorithm for compressible magnetohydrodynamics. Features of the Athena3D code include compressible hydrodynamics and ideal MHD in one, two or three spatial dimensions in Cartesian coordinates; adiabatic and isothermal equations of state; 1st, 2nd or 3rd order reconstruction using the characteristic variables; and numerical fluxes computed using the Roe scheme. In addition, it offers the ability to add source terms to the equations and is parallelized based on MPI.
The ARoMe (Analytical Rossiter-McLaughlin Effects) library generates analytical Rossiter-McLaughlin (RM) effects. It models the Doppler-shift of a star during a transit measured by the fit of a cross-correlation function by a Gaussian function, fit of an observed spectrum by a modeled one, and the weighted mean.
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.
caret (Classification And REgression Training) provides functions for training and plotting classification and regression models. It contains tools for data splitting, pre-processing, feature selection, model tuning using resampling, and variable importance estimation, as well as other functionality.
ASteCA (Automated Stellar Cluster Analysis), written in Python, fully automates standard tests applied on star clusters in order to determine their characteristics, including center, radius, and stars' membership probabilities. It also determines associated intrinsic/extrinsic parameters, including metallicity, age, reddening, distance, total mass, and binarity fraction, among others.
CALCEPH accesses binary planetary ephemeris files, including INPOPxx, JPL DExxx ,and SPICE ephemeris files. It provides a C Application Programming Interface (API) and, optionally, a Fortran 77 or 2003 interface to be called by the application. Two groups of functions enable the access to the ephemeris files, single file access functions, provided to make transition easier from the JPL functions, such as PLEPH, to this library, and many ephemeris file at the same time. Although computers have different endianess (order in which integers are stored as bytes in computer memory), CALCEPH can handles the binary ephemeris files with any endianess by automatically swaps the bytes when it performs read operations on the ephemeris file.
SOAP (Spot Oscillation And Planet) 2.0 simulates the effects of dark spots and bright plages on the surface of a rotating star, computing their expected radial velocity and photometric signatures. It includes the convective blueshift and its inhibition in active regions.
BGLS calculates the Bayesian Generalized Lomb-Scargle periodogram. It takes as input arrays with a time series, a dataset and errors on those data, and returns arrays with sampled periods and the periodogram values at those periods.
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.
D3PO (Denoising, Deconvolving, and Decomposing Photon Observations) addresses the inference problem of denoising, deconvolving, and decomposing photon observations. Its primary goal is the simultaneous but individual reconstruction of the diffuse and point-like photon flux given a single photon count image, where the fluxes are superimposed. A hierarchical Bayesian parameter model is used to discriminate between morphologically different signal components, yielding a diffuse and a point-like signal estimate for the photon flux components.
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.
MRrelation calculates the posterior predictive mass distribution for an individual planet. The probabilistic mass-radius relationship (M-R relation) is evaluated within a Bayesian framework, which both quantifies this intrinsic dispersion and the uncertainties on the M-R relation parameters.
IGMtransmission is a Java graphical user interface that implements Monte Carlo simulations to compute the corrections to colors of high-redshift galaxies due to intergalactic attenuation based on current models of the Intergalactic Medium. The effects of absorption due to neutral hydrogen are considered, with particular attention to the stochastic effects of Lyman Limit Systems. Attenuation curves are produced, as well as colors for a wide range of filter responses and model galaxy spectra. Photometric filters are included for the Hubble Space Telescope, the Keck telescope, the Mt. Palomar 200-inch, the SUBARU telescope and UKIRT; alternative filter response curves and spectra may be readily uploaded.
abcpmc is a Python Approximate Bayesian Computing (ABC) Population Monte Carlo (PMC) implementation based on Sequential Monte Carlo (SMC) with Particle Filtering techniques. It is extendable with k-nearest neighbour (KNN) or optimal local covariance matrix (OLCM) pertubation kernels and has built-in support for massively parallelized sampling on a cluster using MPI.
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.
DPI is a FORTRAN77 library that supplies the symplectic mapping method for binary star systems for the Mercury N-Body software package (ascl:1201.008). The binary symplectic mapping is implemented as a hybrid symplectic method that allows close encounters and collisions between massive bodies and is therefore suitable for planetary accretion simulations.
samiDB is an archive, database, and query engine to serve the spectra, spectral hypercubes, and high-level science products that make up the SAMI Galaxy Survey. Based on the versatile Hierarchical Data Format (HDF5), samiDB does not depend on relational database structures and hence lightens the setup and maintenance load imposed on science teams by metadata tables. The code, written in Python, covers the ingestion, querying, and exporting of data as well as the automatic setup of an HTML schema browser. samiDB serves as a maintenance-light data archive for Big Science and can be adopted and adapted by science teams that lack the means to hire professional archivists to set up the data back end for their projects.
CosmoTransitions analyzes early-Universe finite-temperature phase transitions with multiple scalar fields. The code enables analysis of the phase structure of an input theory, determines the amount of supercooling at each phase transition, and finds the bubble-wall profiles of the nucleated bubbles that drive the transitions.
The self-lensing binary code with Markov chain code was used to analyze the self-lensing binary system KOI-3278. It includes the MCMC modeling and the key figures.
Spearman’s rank correlation test is commonly used in astronomy to discern whether a set of two variables are correlated or not. Unlike most other quantities quoted in astronomical literature, the Spearman’s rank correlation coefficient is generally quoted with no attempt to estimate the errors on its value. This code implements a number of Monte Carlo based methods to estimate the uncertainty on the Spearman’s rank correlation coefficient.
WebbPSF provides a PSF simulation tool in a flexible and easy-to-use software package implemented in Python. Functionality includes support for spectroscopic modes of JWST NIRISS, MIRI, and NIRSpec, including modeling of slit losses and diffractive line spread functions.
drive-casa provides a Python interface for scripting of CASA (ascl:1107.013) subroutines from a separate Python process, allowing for utilization alongside other Python packages which may not easily be installed into the CASA environment. This is particularly useful for embedding use of CASA subroutines within a larger pipeline. drive-casa runs plain-text casapy scripts directly; alternatively, the package includes a set of convenience routines which try to adhere to a consistent style and make it easy to chain together successive CASA reduction commands to generate a command-script programmatically.
Chimenea implements an heuristic algorithm for automated imaging of multi-epoch radio-synthesis data. It generates a deep image via an iterative Clean subroutine performed on the concatenated visibility set and locates steady sources in the field of view. The code then uses this information to apply constrained and then unconstrained (i.e., masked/open-box) Cleans to the single-epoch observations. This obtains better results than if the single-epoch data had been processed independently without prior knowledge of the sky-model. The chimenea pipeline is built upon CASA (ascl:1107.013) subroutines, interacting with the CASA environment via the drive-casa (ascl:1504.006) interface layer.
HOTPANTS (High Order Transform of PSF ANd Template Subtraction) implements the Alard 1999 algorithm for image subtraction. It photometrically aligns one input image with another after they have been astrometrically aligned.
EsoRex (ESO Recipe Execution Tool) lists, configures, and executes Common Pipeline Library (CPL) (ascl:1402.010) recipes from the command line. Its features include automatically generating configuration files, recursive recipe-path searching, command line and configuration file parameters, and recipe product naming control, among many others.
The Solar Position Algorithm (SPA) calculates the solar zenith and azimuth angles in the period from the year -2000 to 6000, with uncertainties of +/- 0.0003 degrees based on the date, time, and location on Earth. SPA is implemented in C; in addition to being available for download, an online calculator using this code is available at http://www.nrel.gov/midc/solpos/spa.html.
UPMASK, written in R, performs membership assignment in stellar clusters. It uses photometry and spatial positions, but can take into account other types of data. UPMASK takes into account arbitrary error models; the code is unsupervised, data-driven, physical-model-free and relies on as few assumptions as possible. The approach followed for membership assessment is based on an iterative process, principal component analysis, a clustering algorithm and a kernel density estimation.
Validation of Exoplanet Signals using a Probabilistic Algorithm (VESPA) calculates false positive probabilities and statistically validates transiting exoplanets. Written in Python, it uses isochrones [ascl:1503.010] and the package simpledist.
Isochrones, written in Python, simplifies common tasks often done with stellar model grids, such as simulating synthetic stellar populations, plotting evolution tracks or isochrones, or estimating the physical properties of a star given photometric and/or spectroscopic observations.
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