Results 1301-1400 of 2645 (2590 ASCL, 55 submitted)
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).
The HERA Librarian system keeps track of all the primary data products for the telescope at a given site. The Librarian supports large data volumes and automated data processing capabilities. A web-based application handles human user and automatic requests and interfaces with a backing database and data storage servers. The system supports the long-term data storage of all relevant telescope data, as well as staging data to individual users' directories for processing.
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.
LIFELINE (LIne proFiles in massivE coLliding wInd biNariEs) simulates the X-ray lines profile in colliding wind binaries. The code is self-consistent and computes the distribution of the wind velocity, the characterization of the wind shock region, and the line profile. In addition to perform the overall computation, LIFELINE can use a pre-computed velocity distribution to compute the shock characteristics and the line profile, or use pre-computed shock characteristics and velocity distributions to compute only the line profile.
light-curve implements the extraction of numerous light curve features suitable for processing alert and archival data for the current ZTF and future Vera Rubin Observatory LSST photometric surveys. These high-performance irregular time series processing tools are written in Rust and Python.
Lightbeam simulates the 3D propagation of light through waveguides of arbitrary geometries. This code package is based off of the finite-differences beam propagation method, and employs a transverse adaptive mesh for extra computational efficiency. Also included are tools to simulate adaptive optics systems for use in conjunction with waveguides, useful in astronomical contexts for simulating coupling devices which transfer telescope light to the science instrument.
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.
Line-Stacker stacks both 3D cubes or already extracted spectra and is an extension of Stacker (ascl:1912.019). It is an ensemble of both CASA tasks and native python tasks. Line-Stacker supports image stacking and some additional tools, allowing further analysis of the stack product, are also included in the module.
linemake generates formatted and curated atomic and molecular line lists suitable for spectral synthesis work. It is lightweight and easy-to-use. The code requires that the requested beginning and ending wavelengths not bridge the divide between two files of atomic line data; in such cases, run the code twice, once on either side of the divide, to generate the desired lists.
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.
LIZARD (Lagrangian Initialization of Zeldovich Amplitudes for Resimulations of Displacements) creates particle initial conditions for cosmological simulations using the Zel'dovich approximation for the matter and velocity power spectrum.
LMC is a Markov Chain Monte Carlo engine in Python that implements adaptive Metropolis-Hastings and slice sampling, as well as the affine-invariant method of Goodman & Weare, in a flexible framework. It can be used for simple problems, but the main use case is problems where expensive likelihood evaluations are provided by less flexible third-party software, which benefit from parallelization across many nodes at the sampling level. The parallel/adaptive methods use communication through MPI, or alternatively by writing/reading files, and mostly follow the approaches pioneered by CosmoMC (ascl:1106.025).
Lmfit provides a high-level interface to non-linear optimization and curve fitting problems for Python. Lmfit builds on and extends many of the optimization algorithm of scipy.optimize, especially the Levenberg-Marquardt method from optimize.leastsq. Its enhancements to optimization and data fitting problems include using Parameter objects instead of plain floats as variables, the ability to easily change fitting algorithms, and improved estimation of confidence intervals and curve-fitting with the Model class. Lmfit includes many pre-built models for common lineshapes.
loci is a shared library for interpolations in up to 4 dimensions. It is written in C and can be used with C/C++, Python and others. In order to calculate the coefficients of the cubic polynom, only local values are used: The data itself and all combinations of first-order derivatives, i.e. in 2D f_x, f_y and f_xy. This is in contrast to splines, where the coefficients are not calculated using derivatives, but non-local data, which can lead to over-smoothing the result.
Locus implements the Locus Algorithm, which maximizes the performance of differential photometry systems by optimizing the number and quality of reference stars in the Field of View with the target.
Lofti_gaia fits orbital parameters for one wide stellar binary relative to the other, when both objects are resolved in Gaia DR2. It takes as input only the Gaia DR2 source id of the two components, and their masses. It retrieves the relevant parameters from the Gaia archive, computes observational constraints for them, and fits orbital parameters to those measurements. It assumes the two components are bound in an elliptical orbit.
LORENE (Langage Objet pour la RElativité NumériquE) solves various problems arising in numerical relativity, and more generally in computational astrophysics. It is a set of C++ classes and provides tools to solve partial differential equations by means of multi-domain spectral methods. LORENE classes implement basic structures such as arrays and matrices, but also abstract mathematical objects, such as tensors, and astrophysical objects, such as stars and black holes.
LOSP is a FORTRAN77 numerical package that computes the orbital parameters of spectroscopic binaries. The package deals with SB1 and SB2 systems and is able to adjust either circular or eccentric orbits through a weighted fit.
LOSSCONE computes the rates of capture of stars by supermassive black holes. It uses a stationary and time-dependent solutions for the Fokker-Planck equation describing the evolution of the distribution function of stars due to two-body relaxation, and works for arbitrary spherical and axisymmetric galactic models that are provided by the user in the form of M(r), the cumulative mass as a function of radius.
We present a set of low resolution empirical SED templates for AGNs and galaxies in the wavelength range from 0.03 to 30 microns based on the multi-wavelength photometric observations of the NOAO Deep-Wide Field Survey Bootes field and the spectroscopic observations of the AGN and Galaxy Evolution Survey. Our training sample is comprised of 14448 galaxies in the redshift range 0<~z<~1 and 5347 likely AGNs in the range 0<~z<~5.58. We use our templates to determine photometric redshifts for galaxies and AGNs. While they are relatively accurate for galaxies, their accuracies for AGNs are a strong function of the luminosity ratio between the AGN and galaxy components. Somewhat surprisingly, the relative luminosities of the AGN and its host are well determined even when the photometric redshift is significantly in error. We also use our templates to study the mid-IR AGN selection criteria developed by Stern et al.(2005) and Lacy et al.(2004). We find that the Stern et al.(2005) criteria suffers from significant incompleteness when there is a strong host galaxy component and at z =~ 4.5, when the broad Halpha emission line is redshifted into the [3.6] band, but that it is little contaminated by low and intermediate redshift galaxies. The Lacy et al.(2004) criterion is not affected by incompleteness at z =~ 4.5 and is somewhat less affected by strong galaxy host components, but is heavily contaminated by low redshift star forming galaxies. Finally, we use our templates to predict the color-color distribution of sources in the upcoming WISE mission and define a color criterion to select AGNs analogous to those developed for IRAC photometry. We estimate that in between 640,000 and 1,700,000 AGNs will be identified by these criteria, but will have serious completeness problems for z >~ 3.4.
LP-VIcode computes variational chaos indicators (CIs) quickly and easily. The following CIs are included:
LPF (Live Pulse Finder) provides real-time automated analysis of the radio image data stream at multiple frequencies. The fully automated GPU-based machine-learning backed pipeline performs source detection, association, flux measurement and physical parameter inference. At the end of the pipeline, an alert of a significant detection of a transient event can be sent out and the data saved for further investigation.
The Limited Post-Newtonian N-body code (LPNN) simulates post-Newtonian interactions between a massive object and many low-mass objects. The interaction between one massive object and low-mass objects is calculated by post-Newtonian approximation, and the interaction between low-mass objects is calculated by Newtonian gravity. This code is based on the sticky9 code, and can be accelerated with the use of GPU in a CUDA (version 4.2 or earlier) environment.
This software computes likelihoods for the Luminous Red Galaxies (LRG) data from the Sloan Digital Sky Survey (SDSS). It includes a patch to the existing CAMB software (ascl:1102.026; the February 2009 release) to calculate the theoretical LRG halo power spectrum for various models. The code is written in Fortran 90 and has been tested with the Intel Fortran 90 and GFortran compilers.
LRGS (Linear Regression by Gibbs Sampling) implements a Gibbs sampler to solve the problem of multivariate linear regression with uncertainties in all measured quantities and intrinsic scatter. LRGS extends an algorithm by Kelly (2007) that used Gibbs sampling for performing linear regression in fairly general cases in two ways: generalizing the procedure for multiple response variables, and modeling the prior distribution of covariates using a Dirichlet process.
LSC (LINEAR Supervised Classification) trains a number of classifiers, including random forest and K-nearest neighbor, to classify variable stars and compares the results to determine which classifier is most successful. Written in R, the package includes anomaly detection code for testing the application of the selected classifier to new data, thus enabling the creation of highly reliable data sets of classified variable stars.
The Large Survey Database (LSD) is a Python framework and DBMS for distributed storage, cross-matching and querying of large survey catalogs (>10^9 rows, >1 TB). The primary driver behind its development is the analysis of Pan-STARRS PS1 data. It is specifically optimized for fast queries and parallel sweeps of positionally and temporally indexed datasets. It transparently scales to more than >10^2 nodes, and can be made to function in "shared nothing" architectures.
LSDCat is a conceptually simple but robust and efficient detection package for emission lines in wide-field integral-field spectroscopic datacubes. The detection utilizes a 3D matched-filtering approach for compact single emission line objects. Furthermore, the software measures fluxes and extents of detected lines. LSDCat is implemented in Python, with a focus on fast processing of large data-volumes.
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.
LTL provides dynamic arrays of up to 7-dimensions, subarrays and slicing, support for fixed-size vectors and matrices including basic linear algebra operations, expression templates-based evaluation, and I/O facilities for ascii and FITS format files. Utility classes for command-line processing and configuration-file processing are provided as well.
LTS_LINEFIT and LTS_PLANEFIT are IDL programs to robustly fit lines and planes to data with intrinsic scatter. The code combines the Least Trimmed Squares (LTS) robust technique, proposed by Rousseeuw (1984) and optimized in Rousseeuw & Driessen (2006), into a least-squares fitting algorithm which allows for intrinsic scatter. This method makes the fit converge to the correct solution even in the presence of a large number of catastrophic outliers, where the much simpler σ-clipping approach can converge to the wrong solution. The code is also available in Python as ltsfit.
LumFunc is a numerical code to model the Luminosity Function based on central galaxy luminosity-halo mass and total galaxy luminosity-halo mass relations. The code can handle rest b_J-band (2dFGRS), r'-band (SDSS), and K-band luminosities, and any redshift with redshift dependences specified by the user. It separates the luminosity function (LF) to conditional luminosity functions, LF as a function of halo mass, and also to galaxy types. By specifying a narrow mass range, the code will return the conditional luminosity functions. The code returns luminosity functions for galaxy types as well (broadly divided to early-type and late-type). The code also models the cluster luminosity function, either mass averaged or for individual clusters.
Long Wavelength Propagation Capability (LWPC), written as a collection of separate programs that perform unique actions, generates geographical maps of signal availability for coverage analysis. The program makes it easy to set up these displays by automating most of the required steps. The user specifies the transmitter location and frequency, the orientation of the transmitting and receiving antennae, and the boundaries of the operating area. The program automatically selects paths along geographic bearing angles to ensure that the operating area is fully covered. The diurnal conditions and other relevant geophysical parameters are then determined along each path. After the mode parameters along each path are determined, the signal strength along each path is computed. The signal strength along the paths is then interpolated onto a grid overlying the operating area. The final grid of signal strength values is used to display the signal-strength in a geographic display. The LWPC uses character strings to control programs and to specify options. The control strings have the same meaning and use among all the programs.
LZIFU (LaZy-IFU) is an emission line fitting pipeline for integral field spectroscopy (IFS) data. Written in IDL, the pipeline turns IFS data to 2D emission line flux and kinematic maps for further analysis. LZIFU has been applied and tested extensively to various IFS data, including the SAMI Galaxy Survey, the Wide-Field Spectrograph (WiFeS), the CALIFA survey, the S7 survey and the MUSE instrument on the VLT.
The MATLAB Astronomy and Astrophysics Toolbox (MAAT) is a collection of software tools and modular functions for astronomy and astrophysics written in the MATLAB environment. It includes over 700 MATLAB functions and a few tens of data files and astronomical catalogs. The scripts cover a wide range of subjects including: astronomical image processing, ds9 control, astronomical spectra, optics and diffraction phenomena, catalog retrieval and searches, celestial maps and projections, Solar System ephemerides, planar and spherical geometry, time and coordinates conversion and manipulation, cosmology, gravitational lensing, function fitting, general utilities, plotting utilities, statistics, and time series analysis.
Photometric rotational modulations due to starspots remain the most common and accessible way to study stellar activity. Modelling rotational modulations allows one to invert the observations into several basic parameters, such as the rotation period, spot coverage, stellar inclination and differential rotation rate. The most widely used analytic model for this inversion comes from Budding (1977) and Dorren (1987), who considered circular, grey starspots for a linearly limb darkened star. That model is extended to be more suitable in the analysis of high precision photometry such as that by Kepler. Macula, a Fortran 90 code, provides several improvements, such as non-linear limb darkening of the star and spot, a single-domain analytic function, partial derivatives for all input parameters, temporal partial derivatives, diluted light compensation, instrumental offset normalisations, differential rotation, starspot evolution and predictions of transit depth variations due to unocculted spots. The inclusion of non-linear limb darkening means macula has a maximum photometric error an order-of-magnitude less than that of Dorren (1987) for Sun-like stars observed in the Kepler-bandpass. The code executes three orders-of-magnitude faster than comparable numerical codes making it well-suited for inference problems.
MADCOW is a set of parallelized programs written in ANSI C and Fortran 77 that perform a maximum likelihood analysis of visibility data from interferometers observing the cosmic microwave background (CMB) radiation. This software has been used to produce power spectra of the CMB with the Very Small Array (VSA) telescope.
MadDM computes dark matter relic abundance and dark matter nucleus scattering rates in a generic model. The code is based on the existing MadGraph 5 architecture and as such is easily integrable into any MadGraph collider study. A simple Python interface offers a level of user-friendliness characteristic of MadGraph 5 without sacrificing functionality. MadDM is able to calculate the dark matter relic abundance in models which include a multi-component dark sector, resonance annihilation channels and co-annihilations. The direct detection module of MadDM calculates spin independent / spin dependent dark matter-nucleon cross sections and differential recoil rates as a function of recoil energy, angle and time. The code provides a simplified simulation of detector effects for a wide range of target materials and volumes.
MADHAT (Model-Agnostic Dark Halo Analysis Tool) analyzes gamma-ray emission from dwarf satellite galaxies and dwarf galaxy candidates due to dark matter annihilation, dark matter decay, or other nonstandard or unknown astrophysics. The tool is data-driven and model-independent, and provides statistical upper bounds on the number of observed photons in excess of the number expected using a stacked analysis of any selected set of dwarf targets. MADHAT also calculates the resulting bounds on the properties of dark matter under any assumptions the user makes regarding dark sector particle physics or astrophysics.
MADLens produces non-Gaussian cosmic shear maps at arbitrary source redshifts. A MADLens simulation with only 256^3 particles produces convergence maps whose power agree with theoretical lensing power spectra up to scales of L=10000. The code is based on a highly parallelizable particle-mesh algorithm and employs a sub-evolution scheme in the lensing projection and a machine-learning inspired sharpening step to achieve these high accuracies.
MADmap produces maximum-likelihood images of the sky from time-ordered data which include correlated noise, such as those gathered by Cosmic Microwave Background (CMB) experiments. It works efficiently on platforms ranging from small workstations to the most massively parallel supercomputers. Map-making is a critical step in the analysis of all CMB data sets, and the maximum-likelihood approach is the most accurate and widely applicable algorithm; however, it is a computationally challenging task. This challenge will only increase with the next generation of ground-based, balloon-borne and satellite CMB polarization experiments. The faintness of the B-mode signal that these experiments seek to measure requires them to gather enormous data sets. MADmap has the ability to address problems typically encountered in the analysis of realistic CMB data sets. The massively parallel and distributed implementation is detailed and scaling complexities are given for the resources required. MADmap is capable of analyzing the largest data sets now being collected on computing resources currently available.
MAESTRO, a low Mach number stellar hydrodynamics code, simulates long-time, low-speed flows that would be prohibitively expensive to model using traditional compressible codes. MAESTRO is based on an equation set derived using low Mach number asymptotics; this equation set does not explicitly track acoustic waves and thus allows a significant increase in the time step. MAESTRO is suitable for two- and three-dimensional local atmospheric flows as well as three-dimensional full-star flows, and adaptive mesh refinement (AMR) has been incorporated into the code. The expansion of the base state for full-star flows using a novel mapping technique between the one-dimensional base state and the Cartesian grid is also available.
NOTE: MAESTRO is no longer being actively developed. Users should switch to MAESTROeX (ascl:1908.019) to take advantage of the latest capabilities.
MAESTROeX solves the equations of low Mach number hydrodynamics for stratified atmospheres or stars with a general equation of state. It includes reactions and thermal diffusion and can be used on anything from a single core to 100,000s of processor cores with MPI + OpenMP. MAESTROeX maintains the accuracy of its predecessor MAESTRO (ascl:1010.044) while taking advantage of a simplified temporal integration scheme and leveraging the AMReX software framework for block-structured adaptive mesh refinement (AMR) applications.
MAGI (MAny-component Galaxy Initializer) generates initial conditions for numerical simulations of galaxies that resemble observed galaxies and are dynamically stable for time-scales longer than their characteristic dynamical times, taking into account galaxy bulges, discs, and haloes. MAGI adopts a distribution-function-based method and supports various kinds of density models, including custom-tabulated inputs and the presence of more than one disc, and is fast and easy to use.
MagIC simulates fluid dynamics in a spherical shell. It solves for the Navier-Stokes equation including Coriolis force, optionally coupled with an induction equation for Magneto-Hydro Dynamics (MHD), a temperature (or entropy) equation and an equation for chemical composition under both the anelastic and the Boussinesq approximations. MagIC uses either Chebyshev polynomials or finite differences in the radial direction and spherical harmonic decomposition in the azimuthal and latitudinal directions. The time-stepping scheme relies on a semi-implicit Crank-Nicolson for the linear terms of the MHD equations and a Adams-Bashforth scheme for the non-linear terms and the Coriolis force.
The R suite magicaxis makes useful and pretty plots for scientific plotting and includes functions for base plotting, with particular emphasis on pretty axis labelling in a number of circumstances that are often used in scientific plotting. It also includes functions for generating images and contours that reflect the 2D quantile levels of the data designed particularly for output of MCMC posteriors where visualizing the location of the 68% and 95% 2D quantiles for covariant parameters is a necessary part of the post MCMC analysis, can generate low and high error bars, and allows clipping of values, rejection of bad values, and log stretching.
MAGIX provides an interface between existing codes and an iterating engine that minimizes deviations of the model results from available observational data; it constrains the values of the model parameters and provides corresponding error estimates. Many models (and, in principle, not only astrophysical models) can be plugged into MAGIX to explore their parameter space and find the set of parameter values that best fits observational/experimental data. MAGIX complies with the data structures and reduction tools of Atacama Large Millimeter Array (ALMA), but can be used with other astronomical and with non-astronomical data.
MAGNETAR is a set of tools for the study of the magnetic field in simulations of MHD turbulence and polarization observations. It calculates the histogram of relative orientation between density structure in the magnetic field in data cubes from simulations of MHD turbulence and observations of polarization using the method of histogram of relative orientations (HRO).
Large-scale coherent magnetic fields are observed in galaxies and clusters, but their ultimate origin remains a mystery. We reconsider the prospects for primordial magnetogenesis by a cosmic string network. We show that the magnetic flux produced by long strings has been overestimated in the past, and give improved estimates. We also compute the fields created by the loop population, and find that it gives the dominant contribution to the total magnetic field strength on present-day galactic scales. We present numerical results obtained by evolving semi-analytic models of string networks (including both one-scale and velocity-dependent one-scale models) in a Lambda-CDM cosmology, including the forces and torques on loops from Hubble redshifting, dynamical friction, and gravitational wave emission. Our predictions include the magnetic field strength as a function of correlation length, as well as the volume covered by magnetic fields. We conclude that string networks could account for magnetic fields on galactic scales, but only if coupled with an efficient dynamo amplification mechanism.
Magnetizer computes time and radial dependent magnetic fields for a sample of galaxies in the output of a semi-analytic model of galaxy formation. The magnetic field is obtained by numerically solving the galactic dynamo equations throughout history of each galaxy. Stokes parameters and Faraday rotation measure can also be computed along a random line-of-sight for each galaxy.
Magnetron, written in Python, decomposes magnetar bursts into a superposition of small spike-like features with a simple functional form, where the number of model components is itself part of the inference problem. Markov Chain Monte Carlo (MCMC) sampling and reversible jumps between models with different numbers of parameters are used to characterize the posterior distributions of the model parameters and the number of components per burst.
MAGPHYS is a self-contained, user-friendly model package to interpret observed spectral energy distributions of galaxies in terms of galaxy-wide physical parameters pertaining to the stars and the interstellar medium. MAGPHYS is optimized to derive statistical constraints of fundamental parameters related to star formation activity and dust content (e.g. star formation rate, stellar mass, dust attenuation, dust temperatures) of large samples of galaxies using a wide range of multi-wavelength observations. A Bayesian approach is used to interpret the SEDs all the way from the ultraviolet/optical to the far-infrared.
Magritte performs 3D radiative transfer modeling; though focused on astrophysics and cosmology, the techniques can also be applied more generally. The code uses a deterministic ray-tracer with a formal solver that currently focuses on line radiative transfer. Magritte can either be used as a C++ library or as a Python package.
MAH calculates the posterior distribution of the "minimum atmospheric height" (MAH) of an exoplanet by inputting the joint posterior distribution of the mass and radius. The code collapses the two dimensions of mass and radius into a one dimensional term that most directly speaks to whether the planet has an atmosphere or not. The joint mass-radius posteriors derived from a fit of some exoplanet data (likely using MCMC) can be used by MAH to evaluate the posterior distribution of R_MAH, from which the significance of a non-zero R_MAH (i.e. an atmosphere is present) is calculated.
MakeCloud makes turbulent giant molecular cloud (GMC) initial conditions for GIZMO (ascl:1410.003). It generates turbulent velocity fields on the fly and stores that data in a user-specified path for efficiency. The code is flexible, allowing the user control through various parameters, including the radius of the cloud, number of gas particles, type of initial turbulent velocity (Gaussian or full), and magnetic energy as a fraction of the binding energy, among other options. With an additional file, it can also create glassy initial conditions.
MaLTPyNT (Matteo's Libraries and Tools in Python for NuSTAR Timing) provides a quick-look timing analysis of NuSTAR data, properly treating orbital gaps and exploiting the presence of two independent detectors by using the cospectrum as a proxy for the power density spectrum. The output of the analysis is a cospectrum, or a power density spectrum, that can be fitted with XSPEC (ascl:9910.005) or ISIS (ascl:1302.002). The software also calculates time lags. Though written for NuSTAR data, MaLTPyNT can also perform standard spectral analysis on X-ray data from other satellite such as XMM-Newton and RXTE.
MALU visualizes integral field spectroscopy (IFS) data such as CALIFA, MANGA, SAMI or MUSE data producing fully interactive plots. The tool is not specific to any instrument. It is available in Python and no installation is required.
The Maneage (Managing data lineage; ending pronounced like "lineage") framework produces fully reproducible computational research. It provides full control on building the necessary software environment from a low-level C compiler, the shell and LaTeX, all the way up to the high-level science software in languages such as Python without a third-party package manager. Once the software environment is built, adding analysis steps is as easy as defining "Make" rules to allow parallelized operations, and not repeating operations that do not need to be recreated. Make provides control over data provenance. A Maneage'd project also contains the narrative description of the project in LaTeX, which helps prepare the research for publication. All results from the analysis are passed into the report through LaTeX macros, allowing immediate dynamic updates to the PDF paper when any part of the analysis has changed. All information is stored in plain text and is version-controlled in Git. Maneage itself is actually a Git branch; new projects start by defining a new Git branch over it and customizing it for a new project. Through Git merging of branches, it is possible to import infrastructure updates to projects.
Mangle deals accurately and efficiently with complex angular masks, such as occur typically in galaxy surveys. Mangle performs the following tasks: converts masks between many handy formats (including HEALPix); rapidly finds the polygons containing a given point on the sphere; rapidly decomposes a set of polygons into disjoint parts; expands masks in spherical harmonics; generates random points with weights given by the mask; and implements computations for correlation function analysis. To mangle, a mask is an arbitrary union of arbitrarily weighted angular regions bounded by arbitrary numbers of edges. The restrictions on the mask are only (1) that each edge must be part of some circle on the sphere (but not necessarily a great circle), and (2) that the weight within each subregion of the mask must be constant. Mangle is complementary to and integrated with the HEALPix package (ascl:1107.018); mangle works with vector graphics whereas HEALPix works with pixels.
The MapCUMBA package applies a multigrid fast iterative Jacobi algorithm for map-making in the context of CMB experiments.
MapCurvature, written in IDL, can create map projections with Goldberg-Gott indicatrices. These indicatrices measure the flexion and skewness of a map, and are useful for determining whether features are faithfully reproduced on a particular projection.
MAPPINGS III is a general purpose astrophysical plasma modelling code. It is principally intended to predict emission line spectra of medium and low density plasmas subjected to different levels of photoionization and ionization by shockwaves. MAPPINGS III tracks up to 16 atomic species in all stages of ionization, over a useful range of 102 to 108 K. It treats spherical and plane parallel geometries in equilibrium and time-dependent models. MAPPINGS III is useful for computing models of HI and HII regions, planetary nebulae, novae, supernova remnants, Herbig-Haro shocks, active galaxies, the intergalactic medium and the interstellar medium in general. The present version of MAPPINGS III is a large FORTRAN program that runs with a simple TTY interface for historical and portability reasons.
MAPPINGS V is a update of the MAPPINGS code (ascl:1306.008) and provides new cooling function computations for optically thin plasmas based on the greatly expanded atomic data of the CHIANTI 8 database. The number of cooling and recombination lines has been expanded from ~2000 to over 80,000, and temperature-dependent spline-based collisional data have been adopted for the majority of transitions. The expanded atomic data set provides improved modeling of both thermally ionized and photoionized plasmas; the code is now capable of predicting detailed X-ray spectra of nonequilibrium plasmas over the full nonrelativistic temperature range, increasing its utility in cosmological simulations, in modeling cooling flows, and in generating accurate models for the X-ray emission from shocks in supernova remnants.
MAPS (Multi-frequency Angular Power Spectrum) extracts two-point statistical information from Epoch of Reionization (EoR) signals observed in three dimensions, with two directions on the sky and the wavelength (or frequency) constituting the third dimension. Rather than assume that the signal has the same statistical properties in all three directions, as the spherically averaged power spectrum (SAPS) does, MAPS does not make these assumptions, making it more natural for radio interferometric observations than SAPS.
MARGE (Machine learning Algorithm for Radiative transfer of Generated Exoplanets) generates exoplanet spectra across a defined parameter space, processes the output, and trains, validates, and tests machine learning models as a fast approximation to radiative transfer. It uses BART (ascl:1608.004) for spectra generation and modifies BART’s Bayesian sampler (MC3, ascl:1610.013) with a random uniform sampler to propose models within a defined parameter space. More generally, MARGE provides a framework for training neural network models to approximate a forward, deterministic process.
With the commissioning of the second MAGIC gamma-ray Cherenkov telescope situated close to MAGIC-I, the standard analysis package of the MAGIC collaboration, MARS, has been upgraded in order to perform the stereoscopic reconstruction of the detected atmospheric showers. MARS is a ROOT-based code written in C++, which includes all the necessary algorithms to transform the raw data recorded by the telescopes into information about the physics parameters of the observed targets. An overview of the methods for extracting the basic shower parameters is presented, together with a description of the tools used in the background discrimination and in the estimation of the gamma-ray source spectra.
MarsLux generates illumination maps of Mars from Digital Terrain Model (DTM), permitting users to investigate in detail the illumination conditions on Mars based on its topography and the relative position of the Sun. MarsLux consists of two Python codes, SolaPar and MarsLux. SolaPar calculates the matrix with solar parameters for one date or a range between the two. The Marslux code generates the illumination maps using the same DTM and the files generated by SolaPar. The resulting illumination maps show areas that are fully illuminated, areas in total shadow, and areas with partial shade, and can be used for geomorphological studies to examine gullies, thermal weathering, or mass wasting processes as well as for producing energy budget maps for future exploration missions.
MARTINI (Mock APERTIF-like Radio Telescope Interferometry of the Neutal ISM) creates synthetic resolved HI line observations (data cubes) of smoothed-particle hydrodynamics simulations of galaxies. The various aspects of the mock-observing process are divided logically into sub-modules handling the data cube, source, beam, noise, spectral model and SPH kernel. MARTINI is object-oriented: each sub-module provides a class (or classes) which can be configured as desired. For most sub-modules, base classes are provided to allow for straightforward customization. Instances of each sub-module class are then given as parameters to the Martini class. A mock observation is then constructed by calling a handful of functions to execute the desired steps in the mock-observing process.
Marvin searches, accesses, and visualizes data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. Written in Python, it provides tools for easy efficient interaction with the MaNGA data via local files, files retrieved from the Science Archive Server, or data directly grabbed from the database. The tools come mainly in the form of convenience functions and classes for interacting with the data. Also available is a web app, Marvin-web, offers an easily accessible interface for searching the MaNGA data and visual exploration of individual MaNGA galaxies or of the entire sample, and a powerful query functionality that uses the API to query the MaNGA databases and return the search results to your python session. Marvin-API is the critical link that allows Marvin-tools and Marvin-web to interact with the databases, which enables users to harness the statistical power of the MaNGA data set.
MARX (Model of AXAF Response to X-rays) is a suite of programs designed to enable the user to simulate the on-orbit performance of the Chandra satellite. MARX provides a detailed ray-trace simulation of how Chandra responds to a variety of astrophysical sources and can generate standard FITS events files and images as output. It contains models for the HRMA mirror system onboard Chandra as well as the HETG and LETG gratings and all focal plane detectors.
MARXS (Multi-Architecture-Raytrace-Xraymission-Simulator) simulates X-ray observatories. Primarily designed to simulate X-ray instruments on astronomical X-ray satellites and sounding rocket payloads, it can also be used to ray-trace experiments in the laboratory. MARXS performs polarization Monte-Carlo ray-trace simulations from a source (astronomical or lab) through a collection of optical elements such as mirrors, baffles, and gratings to a detector.
MARZ analyzes objects and produces high quality spectroscopic redshift measurements. Spectra not matched correctly by the automatic algorithm can be redshifted manually by cycling automatic results, manual template comparison, or marking spectral features. The software has an intuitive interface and powerful automatic matching capabilities on spectra, and can be run interactively or from the command line, and runs as a Web application. MARZ can be run on a local server; it is also available for use on a public server.
Mask galaxy is an automatic machine learning pipeline for detection, segmentation and morphological classification of galaxies. The model is based on the Mask R-CNN Deep Learning architecture. This model of instance segmentation also performs image segmentation at the pixel level, and has shown a Mean Average Precision (mAP) of 0.93 in morphological classification of spiral or elliptical galaxies.
The detection of B-mode polarization in the CMB is one of the most important outstanding tests of inflationary cosmology. One of the necessary steps for extracting polarization information in the CMB is reducing contamination from so-called "ambiguous modes" on a masked sky, which contain leakage from the larger E-mode signal. This can be achieved by utilising derivative operators on the real-space Stokes Q and U parameters. This paper presents an algorithm and a software package to perform this procedure on the nearly full sky, i.e., with projects such as the Planck Surveyor and future satellites in mind; in particular, the package can perform finite differences on masked, irregular grids and is applied to a semi-regular spherical pixellization, the HEALPix grid. The formalism reduces to the known finite-difference solutions in the case of a regular grid. We quantify full-sky improvements on the possible bounds on the CMB B-mode signal. We find that in the specific case of E and B-mode separation, there exists a "pole problem" in our formalism which produces signal contamination at very low multipoles l. Several solutions to the "pole problem" are presented; one proposed solution facilitates a calculation of a general Gaussian quadrature scheme, which finds application in calculating accurate harmonic coefficients on the HEALPix sphere. Nevertheless, on a masked sphere the software represents a considerable reduction in B-mode noise from limited sky coverage.
MASSCLEAN is a sophisticated and robust stellar cluster image and photometry simulation package. This package is able to create color-magnitude diagrams and standard FITS images in any of the traditional optical and near-infrared bands based on cluster characteristics input by the user, including but not limited to distance, age, mass, radius and extinction. At the limit of very distant, unresolved clusters, we have checked the integrated colors created in MASSCLEAN against those from other simple stellar population (SSP) models with consistent results. Because the algorithm populates the cluster with a discrete number of tenable stars, it can be used as part of a Monte Carlo Method to derive the probabilistic range of characteristics (integrated colors, for example) consistent with a given cluster mass and age.
massconvert, written in Fortran, provides driver and fitting routines for converting halo mass definitions from one spherical overdensity to another assuming an NFW density profile. In surveys that probe ever lower cluster masses and temperatures, sample variance is generally comparable to or greater than shot noise and thus cannot be neglected in deriving precision cosmological constraints; massconvert offers an accurate fitting formula for the conversion between different definitions of halo mass.
MATCH matches up items in two different lists, which can have two different systems of coordinates. The program allows the two sets of coordinates to be related by a linear, quadratic, or cubic transformation. MATCH was designed and written to work on lists of stars and other astronomical objects but can be applied to other types of data. In order to match two lists of N points, the main algorithm calls for O(N^6) operations; though not the most efficient choice, it does allow for arbitrary translation, rotation, and scaling.
A discrete Point Spread Function (PSF) is a sampled version of a continuous two-dimensional PSF. The shape information about the photon scattering pattern of a discrete PSF is typically encoded using a numerical table (matrix) or a FITS image file. MATPHOT shifts discrete PSFs within an observational model using a 21-pixel- wide damped sinc function and position partial derivatives are computed using a five-point numerical differentiation formula. MATPHOT achieves accurate and precise stellar photometry and astrometry of undersampled CCD observations by using supersampled discrete PSFs that are sampled two, three, or more times more finely than the observational data.
maxsmooth fits derivative constrained functions (DCF) such as Maximally Smooth Functions (MSFs) to data sets. MSFs are functions for which there are no zero crossings in derivatives of order m >= 2 within the domain of interest. They are designed to prevent the loss of signals when fitting out dominant smooth foregrounds or large magnitude signals that mask signals of interest. Here "smooth" means that the foregrounds follow power law structures and do not feature turning points in the band of interest. maxsmooth uses quadratic programming implemented with CVXOPT (ascl:2008.017) to fit data subject to a fixed linear constraint, Ga <= 0, where the product Ga is a matrix of derivatives. The code tests the <= 0 constraint multiplied by a positive or negative sign and can test every available sign combination but by default, it implements a sign navigating algorithm.
Mayavi provides general-purpose 3D scientific visualizations. It offers easy interactive tools for data visualization that fit with the scientific user's workflow. Mayavi provides several entry points: a full-blown interactive application; a Python library with both a MATLAB-like interface focused on easy scripting and a feature-rich object hierarchy; widgets associated with these objects for assembling in a domain-specific application, and plugins that work with a general purpose application-building framework.
Mbb_emcee fits modified blackbodies to photometry data using an affine invariant MCMC. It has large number of options which, for example, allow computation of the IR luminosity or dustmass as part of the fit. Carrying out a fit produces a HDF5 output file containing the results, which can either be read directly, or read back into a mbb_results object for analysis. Upper and lower limits can be imposed as well as Gaussian priors on the model parameters. These additions are useful for analyzing poorly constrained data. In addition to standard Python packages scipy, numpy, and cython, mbb_emcee requires emcee (ascl:1303.002), Astropy (ascl:1304.002), h5py, and for unit tests, nose.
MOLSCAT, which supercedes MOLSCAT version 14 (ascl:1206.004), performs non-reactive quantum scattering calculations for atomic and molecular collisions using coupled-channel methods. Simple atom-molecule and molecule-molecule collision types are coded internally and additional ones may be handled with plug-in routines. Plug-in routines may include external magnetic, electric or photon fields (and combinations of them).
The package also includes BOUND, which performs calculations of bound-state energies in weakly bound atomic and molecular systems using coupled-channel methods, and FIELD, a development of BOUND that locates values of external fields at which a bound state exists with a specified energy. Though the three programs have different applications, they use closely related methods, share many subroutines, and are released with a single code base.
MBProj2 obtains thermodynamic profiles of galaxy clusters. It forward-models cluster X-ray surface brightness profiles in multiple bands, optionally assuming hydrostatic equilibrium. The code is a set of Python classes the user can use or extend. When modelling a cluster assuming hydrostatic equilibrium, the user chooses a form for the density profile (e.g. binning or a beta model), the metallicity profile, and the dark matter profile (e.g. NFW). If hydrostatic equilibrium is not assumed, a temperature profile model is used instead of the dark matter profile. The code uses the emcee Markov Chain Monte Carlo code (ascl:1303.002) to sample the model parameters, using these to produce chains of thermodynamic profiles.
MC-SPAM (Monte-Carlo Synthetic-Photometry/Atmosphere-Model) generates limb-darkening coefficients from models that are comparable to transit photometry; it extends the original SPAM algorithm by Howarth (2011) by taking in consideration the uncertainty on the stellar and transit parameters of the system under analysis.
MC3 (Multi-core Markov-chain Monte Carlo) is a Bayesian statistics tool that can be executed from the shell prompt or interactively through the Python interpreter with single- or multiple-CPU parallel computing. It offers Markov-chain Monte Carlo (MCMC) posterior-distribution sampling for several algorithms, Levenberg-Marquardt least-squares optimization, and uniform non-informative, Jeffreys non-informative, or Gaussian-informative priors. MC3 can share the same value among multiple parameters and fix the value of parameters to constant values, and offers Gelman-Rubin convergence testing and correlated-noise estimation with time-averaging or wavelet-based likelihood estimation methods.
MC3D is a 3D continuum radiative transfer code; it is based on the Monte-Carlo method and solves the radiative transfer problem self-consistently. It is designed for the simulation of dust temperatures in arbitrary geometric configurations and the resulting observables: spectral energy distributions, wavelength-dependent images, and polarization maps. The main objective is the investigation of "dust-dominated" astrophysical systems such as young stellar objects surrounded by an optically thick circumstellar disk and an optically thin(ner) envelope, debris disks around more evolved stars, asymptotic giant branch stars, the dust component of the interstellar medium, and active galactic nuclei.
MCAL calculates high precision metallicities and effective temperatures for M dwarfs; the method behaves properly down to R = 40 000 and S/N = 25, and results were validated against a sample of stars in common with SOPHIE high resolution spectra.
MCALF (Multi-Component Atmospheric Line Fitting) accurately constrains velocity information from spectral imaging observations using machine learning techniques. It is useful for solar physicists trying to extract line-of-sight (LOS) Doppler velocity information from spectral imaging observations (Stokes I measurements) of the Sun. A toolkit is provided that can be used to define a spectral model optimized for a particular dataset. MCALF is particularly suited for extracting velocity information from spectral imaging observations where the individual spectra can contain multiple spectral components. Such multiple components are typically present when active solar phenomenon occur within an isolated region of the solar disk. Spectra within such a region will often have a large emission component superimposed on top of the underlying absorption spectral profile from the quiescent solar atmosphere.
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