Results 1101-1150 of 2450 (2411 ASCL, 39 submitted)
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.
MissFITS is a program that performs basic maintenance and packaging tasks on FITS files using an optimized FITS library. MissFITS can:
MIS is a pipeline toolkit using the package MIRIAD to combine Interferometric and Single Dish data. This was prompted by our observations made with the Combined Array For Research in Millimeter-wave Astronomy (CARMA) interferometer of the star-forming region NGC 1333, a large survey highlighting the new 23-element and singledish observing modes. The project consists of 20 CARMA datasets each containing interferometric as well as simultaneously obtained single dish data, for 3 molecular spectral lines and continuum, in 527 different pointings, covering an area of about 8 by 11 arcminutes. A small group of collaborators then shared this toolkit and their parameters via CVS, and scripts were developed to ensure uniform data reduction across the group. The pipeline was run end-to-end each night that new observations were obtained, producing maps that contained all the data to date. This approach could serve as a model for repeated calibration and mapping of large mixed-mode correlation datasets from ALMA.
mirkwood uses supervised machine learning to model non-linearly mapping galaxy fluxes to their properties. Multiple models are stacked to mitigate poor performance by any individual model in a given region of the parameter space. The code accounts for uncertainties arising both from intrinsic noise in observations and from finite training data and incorrect modeling assumptions, and provides highly accurate physical properties from observations of galaxies as compared to traditional SED fitting.
MIRIAD is a radio interferometry data-reduction package, designed for taking raw visibility data through calibration to the image analysis stage. It has been designed to handle any interferometric array, with working examples for BIMA, CARMA, SMA, WSRT, and ATCA. A separate version for ATCA is available, which differs in a few minor ways from the CARMA version.
MiraPy is a Python package for problem-solving in astronomy using Deep Learning for astrophysicist, researchers and students. Current applications of MiraPy are X-Ray Binary classification, ATLAS variable star feature classification, OGLE variable star light-curve classification, HTRU1 dataset classification and Astronomical image reconstruction using encoder-decoder network. It also contains modules for loading various datasets, curve-fitting, visualization and other utilities. It is built using Keras for developing ML models to run on CPU and GPU seamlessly.
minot (Modeling of the ICM (Non-)thermal content and Observables prediction Tools) provides a self-consistent modeling framework for the thermal and non-thermal diffuse components in galaxy clusters and predictions multi-wavelength observables. The framework sets or modifies the cluster object according to set parameters and defines the physical and observational properties, which can include thermal gas and CR physics, tSZ, inverse Compton, and radio synchotron. minot then generates outputs, including model parameters, plots, and relationships between models.
Minerva is a cylindrical coordinate extension of the Athena astrophysical MHD code of Stone, Gardiner, Teuben, and Hawley. The extension follows the approach of Athena's original developers and has been designed to alter the existing Cartesian-coordinates code as minimally and transparently as possible. The numerical equations in cylindrical coordinates are formulated to maintain consistency with constrained transport (CT), a central feature of the Athena algorithm, while making use of previously implemented code modules such as the Riemann solvers. Angular momentum transport, which is critical in astrophysical disk systems dominated by rotation, is treated carefully.
Min-CaLM performs automated mineral compositional analysis on debris disk spectra. The user inputs the debris disk spectrum, and using Min-CaLM's built-in mineralogical library, Min-CaLM calculates the relative mineral abundances within the disk. To do this calculation, Min-CaLM converts the debris disk spectrum and the mineralogical library spectra into a system of linear equations, which it then solves using non-negative least square minimization. This code comes with a GitHub tutorial on how to use the Min-CaLM package.
miluphcuda is the CUDA port of the original miluph code; it runs on single Nvidia GPUs with compute capability 5.0 and higher and provides fast and efficient computation. The code can be used for hydrodynamical simulations and collision and impact physics, and features self-gravity via Barnes-Hut trees and porosity models such as P-alpha and epsilon-alpha. It can model solid bodies, including ductile and brittle materials, as well as non-viscous fluids, granular media, and porous continua.
The millisearch.for code was used to generate a new search for the gravitational lens effects of a significant cosmological density of supermassive compact objects (SCOs) on gamma-ray bursts. No signal attributable to millilensing was found. We inspected the timing data of 774 BATSE-triggered GRBs for evidence of millilensing: repeated peaks similar in light-curve shape and spectra. Our null detection leads us to conclude that, in all candidate universes simulated, OmegaSCO < 0.1 is favored for 105 < MSCO/Modot < 109, while in some universes and mass ranges the density limits are as much as 10 times lower. Therefore, a cosmologically significant population of SCOs near globular cluster mass neither came out of the primordial universe, nor condensed at recombination.
millennium-tap-query is a simple wrapper for the Python package requests to deal with connections to the Millennium TAP Web Client. With this tool you can perform basic or advanced queries to the Millennium Simulation database and download the data products. millennium-tap-query is similar to the TAP query tool in the German Astrophysical Virtual Observatory (GAVO) VOtables package.
MillCgs clusters galaxies from the semi-analytic models run on top of the Millennium Simulation to identify Compact Groups. MillCgs uses a machine learning clustering algorithm to find the groups and then runs analytics to filter out the groups that do not fit the user specified criteria. The package downloads the data, processes it, and then creates graphs of the data.
This triggering code calculates the correlation function between two astrophysical data catalogs using the Landy-Szalay approximator generalized for heterogeneous datasets (Landy & Szalay, 1993; Bradshaw et al, 2011) or the auto-correlation function of one dataset. It assumes that one catalog has positional information as well as an object size (effective radius), and the other only positional information.
Miex calculates Mie scattering coefficients and efficiency factors for broad grain size distributions and a very wide wavelength range (λ ≈ 10-10-10-2m) of the interacting radiation and incorporates standard solutions of the scattering amplitude functions. The code handles arbitrary size parameters, and single scattering by particle ensembles is calculated by proper averaging of the respective parameters.
The Markwardt IDL Library contains routines for curve fitting and function minimization, including MPFIT (ascl:1208.019), statistical tests, and non-linear optimization (TNMIN); graphics programs including plotting three-dimensional data as a cube and fixed- or variable-width histograms; adaptive numerical integration (Quadpack), Chebyshev approximation and interpolation, and other mathematical tools; many ephemeris and timing routines; and array and set operations, such as computing the fast product of a large array, efficiently inserting or deleting elements in an array, and performing set operations on numbers and strings; and many other useful and varied routines.
midIR_sensitivity is IDL code that calculates the sensitivity of a ground-based mid-infrared instrument for astronomy. The code was written for the Phase A study of the instrument METIS (http://www.strw.leidenuniv.nl/metis), the Mid-Infrared E-ELT Imager and Spectrograph, for the 42-m European Extremely Large Telescope. The model uses a detailed set of input parameters for site characteristics and atmospheric profiles, optical design, and thermal background. The code and all input parameters are highly tailored for the particular design parameters of the E-ELT and METIS, however, the program is structured in such a way that the parameters can easily be adjusted for a different system, or alternative input files used.
micrOMEGAs calculates the properties of cold dark matter in a generic model of particle physics. First developed to compute the relic density of dark matter, the code also computes the rates for dark matter direct and indirect detection. The code provides the mass spectrum, cross-sections, relic density and exotic fluxes of gamma rays, positrons and antiprotons. The propagation of charged particles in the Galactic halo is handled with a module that allows to easily modify the propagation parameters. The cross-sections for both spin dependent and spin independent interactions of WIMPS on protons are computed automatically as well as the rates for WIMP scattering on nuclei in a large detector. Annihilation cross-sections of the dark matter candidate at zero velocity, relevant for indirect detection of dark matter, are computed automatically, and the propagation of charged particles in the Galactic halo is also handled.
Occultation and microlensing are different limits of the same phenomena of one body passing in front of another body. We derive a general exact analytic expression which describes both microlensing and occultation in the case of spherical bodies with a source of uniform brightness and a non-relativistic foreground body. We also compute numerically the case of a source with quadratic limb-darkening. In the limit that the gravitational deflection angle is comparable to the angular size of the foreground body, both microlensing and occultation occur as the objects align. Such events may be used to constrain the size ratio of the lens and source stars, the limb-darkening coefficients of the source star, and the surface gravity of the lens star (if the lens and source distances are known). Application of these results to microlensing during transits in binaries and giant-star microlensing are discussed. These results unify the microlensing and occultation limits and should be useful for rapid model fitting of microlensing, eclipse, and "microccultation" events.
michi2 fits combinations of arbitrary numbers of libraries/components to a given observational data. Written in C++ and Python, this chi-square fitting tool can fit a galaxy's spectral energy distribution (SED) with stellar, active galactic nuclear, dust and radio SED templates, and fit a galaxy's spectral line energy distribution (SLED) with one or more gas components using radiative transfer LVG model grid libraries.
michi2 first samples the high-dimensional parameter space (N1*N2*N3*..., where N is the number of independent templates in each library, and 1/2/3 is the ID of components) in an optimized way for a few thousand or tens of thousand times to compute the chi-square to the input observational data, then uses Python scripts to analyze the chi-square distribution and derive the best-fit, median, lower and higher 1-sigma values for each parameter in each library/component. This tool is useful for fitting larger number of templates and arbitrary combinations of libraries/components, including some constraining of one library/component onto another.
MIA+EWS is a package of two data reduction tools for MIDI data which uses power-spectrum analysis or the information contained in the spectrally-dispersed fringe measurements in order to estimate the correlated flux and the visibility as function of wavelength in the N-band. MIA, which stands for MIDI Interactive Analysis, uses a Fast Fourier Transformation to calculate the Fourier amplitudes of the fringe packets to calculate the correlated flux and visibility. EWS stands for Expert Work-Station, which is a collection of IDL tools to apply coherent visibility analysis to reduce MIDI data. The EWS package allows the user to control and examine almost every aspect of MIDI data and its reduction. The usual data products are the correlated fluxes, total fluxes and differential phase.
MHF is a Dark Matter halo finder that is based on the refinement grids of MLAPM. The grid structure of MLAPM adaptively refines around high-density regions with an automated refinement algorithm, thus naturally "surrounding" the Dark Matter halos, as they are simply manifestations of over-densities within (and exterior) to the underlying host halo. Using this grid structure, MHF restructures the hierarchy of nested isolated MLAPM grids into a "grid tree". The densest cell in the end of a tree branch marks center of a prospective Dark Matter halo. All gravitationally bound particles about this center are collected to obtain the final halo catalog. MHF automatically finds halos within halos within halos.
MGHalofit is a modified gravity extension of the fitting formula for the matter power spectrum of HALOFIT and its improvement by Takahashi et al. MGHalofit is implemented in MGCAMB, which is based on CAMB. MGHalofit calculates the nonlinear matter power spectrum P(k) for the Hu-Sawicki model. Comparing MGHalofit predictions at various redshifts (z<=1) to the f(R) simulations, the accuracy on P(k) is 6% at k<1 h/Mpc and 12% at 1<k<10 h/Mpc respectively.
We have developed MGGPOD, a user-friendly suite of Monte Carlo codes built around the widely used GEANT (Version 3.21) package. The MGGPOD Monte Carlo suite and documentation are publicly available for download. MGGPOD is an ideal tool for supporting the various stages of gamma-ray astronomy missions, ranging from the design, development, and performance prediction through calibration and response generation to data reduction. In particular, MGGPOD is capable of simulating ab initio the physical processes relevant for the production of instrumental backgrounds. These include the build-up and delayed decay of radioactive isotopes as well as the prompt de-excitation of excited nuclei, both of which give rise to a plethora of instrumental gamma-ray background lines in addition to continuum backgrounds.
MGE_FIT_SECTORS performs Multi-Gaussian Expansion (MGE) fits to galaxy images. The MGE parameterizations are useful in the construction of realistic dynamical models of galaxies, PSF deconvolution of images, the correction and estimation of dust absorption effects, and galaxy photometry. The algorithm is well suited for use with multiple-resolution images (e.g. Hubble Space Telescope (HST) and ground-based images).
CAMB is a public Fortran 90 code written by Antony Lewis and Anthony Challinor for evaluating cosmological observables. MGCAMB is a modified version of CAMB in which the linearized Einstein equations of General Relativity (GR) are modified. MGCAMB can also be used in CosmoMC to fit different modified-gravity (MG) models to data.
MGB (Marxist Ghost Buster) attacks spectral classification by using an interactive comparison with spectral libraries. It allows the user to move along the two traditional dimensions of spectral classification (spectral subtype and luminosity classification) plus the two additional ones of rotation index and spectral peculiarities. Double-lined spectroscopic binaries can also be fitted using a combination of two standards. The code includes OB2500 v2.0, a standard grid of blue-violet R ~ 2500 spectra of O stars from the Galactic O-Star Spectroscopic Survey, but other grids can be added to MGB.
The Meudon PDR code computes the atomic and molecular structure of interstellar clouds. It can be used to study the physics and chemistry of diffuse clouds, photodissociation regions (PDRs), dark clouds, or circumstellar regions. The model computes the thermal balance of a stationary plane-parallel slab of gas and dust illuminated by a radiation field and takes into account heating processes such as the photoelectric effect on dust, chemistry, cosmic rays, etc. and cooling resulting from infrared and millimeter emission of the abundant species. Chemistry is solved for any number of species and reactions. Once abundances of atoms and molecules and level excitation of the most important species have been computed at each point, line intensities and column densities can be deduced.
MESS is a Monte Carlo simulation IDL code which uses either the results of the statistical analysis of the properties of discovered planets, or the results of the planet formation theories, to build synthetic planet populations fully described in terms of frequency, orbital elements and physical properties. They can then be used to either test the consistency of their properties with the observed population of planets given different detection techniques or to actually predict the expected number of planets for future surveys. It can be used to probe the physical and orbital properties of a putative companion within the circumstellar disk of a given star and to test constrain the orbital distribution properties of a potential planet population around the members of the TW Hydrae association. Finally, using in its predictive mode, the synergy of future space and ground-based telescopes instrumentation has been investigated to identify the mass-period parameter space that will be probed in future surveys for giant and rocky planets. A Python version of this code, Exo-DMC (ascl:2010.008), is available.
Meso-NH is the non-hydrostatic mesoscale atmospheric model of the French research community jointly developed by the Laboratoire d'Aérologie (UMR 5560 UPS/CNRS) and by CNRM (UMR 3589 CNRS/Météo-France). Meso-NH incorporates a non-hydrostatic system of equations for dealing with scales ranging from large (synoptic) to small (large eddy) scales while calculating budgets and has a complete set of physical parameterizations for the representation of clouds and precipitation. It is coupled to the surface model SURFEX for representation of surface atmosphere interactions by considering different surface types (vegetation, city, ocean, lake) and allows a multi-scale approach through a grid-nesting technique. Meso-NH is versatile, vectorized, parallelized, and operates in 1D, 2D or 3D; it is coupled with a chemistry module (including gas-phase, aerosol, and aqua-phase components) and a lightning module, and has observation operators that compare model output directly with satellite observations, radar, lidar and GPS.
MeshLab processes and edits 3D triangular meshes. It includes tools for editing, cleaning, healing, inspecting, rendering, texturing and converting meshes, and offers features for processing raw data produced by 3D digitization tools and devices and for preparing models for 3D printing.
Stellar physics and evolution calculations enable a broad range of research in astrophysics. Modules for Experiments in Stellar Astrophysics (MESA) is a suite of open source libraries for a wide range of applications in computational stellar astrophysics. A newly designed 1-D stellar evolution module, MESA star, combines many of the numerical and physics modules for simulations of a wide range of stellar evolution scenarios ranging from very-low mass to massive stars, including advanced evolutionary phases. MESA star solves the fully coupled structure and composition equations simultaneously. It uses adaptive mesh refinement and sophisticated timestep controls, and supports shared memory parallelism based on OpenMP. Independently usable modules provide equation of state, opacity, nuclear reaction rates, and atmosphere boundary conditions. Each module is constructed as a separate Fortran 95 library with its own public interface. Examples include comparisons to other codes and show evolutionary tracks of very low mass stars, brown dwarfs, and gas giant planets; the complete evolution of a 1 Msun star from the pre-main sequence to a cooling white dwarf; the Solar sound speed profile; the evolution of intermediate mass stars through the thermal pulses on the He-shell burning AGB phase; the interior structure of slowly pulsating B Stars and Beta Cepheids; evolutionary tracks of massive stars from the pre-main sequence to the onset of core collapse; stars undergoing Roche lobe overflow; and accretion onto a neutron star.
Merger Trees uses a Monte Carlo algorithm to generate merger trees describing the formation history of dark matter haloes; the algorithm is implemented in Fortran. The algorithm is a modification of the algorithm of Cole et al. used in the GALFORM semi-analytic galaxy formation model (ascl:1510.005) based on the Extended Press–Schechter theory. It should be applicable to hierarchical models with a wide range of power spectra and cosmological models. It is tuned to be in accurate agreement with the conditional mass functions found in the analysis of merger trees extracted from the Λ cold dark matter Millennium N-body simulation. The code should be a useful tool for semi-analytic models of galaxy formation and for modelling hierarchical structure formation in general.
Mercury is a new general-purpose software package for carrying out orbital integrations for problems in solar-system dynamics. Suitable applications include studying the long-term stability of the planetary system, investigating the orbital evolution of comets, asteroids or meteoroids, and simulating planetary accretion. Mercury is designed to be versatile and easy to use, accepting initial conditions in either Cartesian coordinates or Keplerian elements in "cometary" or "asteroidal" format, with different epochs of osculation for different objects. Output from an integration consists of osculating elements, written in a machine-independent compressed format, which allows the results of a calculation performed on one platform to be transferred (e.g. via FTP) and decoded on another.
During an integration, Mercury monitors and records details of close encounters, sungrazing events, ejections and collisions between objects. The effects of non-gravitational forces on comets can also be modeled. The package supports integrations using a mixed-variable symplectic routine, the Bulirsch-Stoer method, and a hybrid code for planetary accretion calculations.
Mercury-T calculates the evolution of semi-major axis, eccentricity, inclination, rotation period and obliquity of the planets as well as the rotation period evolution of the host body; it is based on the N-body code Mercury (Chambers 1999, ascl:1201.008). It is flexible, allowing computation of the tidal evolution of systems orbiting any non-evolving object (if its mass, radius, dissipation factor and rotation period are known), but also evolving brown dwarfs (BDs) of mass between 0.01 and 0.08 M⊙, an evolving M-dwarf of 0.1 M⊙, an evolving Sun-like star, and an evolving Jupiter.
MERA works with large 3D AMR/uniform-grid and N-body particle data sets from astrophysical simulations such as those produced by the hydrodynamic code RAMSES (ascl:1011.007) and is written entirely in the Julia language. The package provides essential functions for efficient and memory lightweight data loading and analysis. The core of MERA is a database framework.
MeqTrees is a software package for implementing Measurement Equations. This makes it uniquely suited for simulation and calibration of radioastronomical data, especially that involving new radiotelescopes and observational regimes. MeqTrees is implemented as a Python-based front-end called the meqbrowser, and an efficient (C++-based) computational back-end called the meqserver. Numerical models are defined on the front-end via a Python-based Tree Definition Language (TDL), then rapidly executed on the back-end. The use of TDL facilitates an extremely short turn-around time for experimentation with new ideas. This is also helped by unprecedented visualization capabilities for all final and intermediate results. A flexible data model and a number of important optimizations in the back-end ensures that the numerical performance is comparable to that of hand-written code.
MeqTrees includes a highly capable FITS viewer and sky model manager called Tigger, which can also work as a standalone tool.
MEPSA (Multiple Excess Peak Search Algorithm) identifies peaks within a uniformly sampled time series affected by uncorrelated Gaussian noise. MEPSA scans the time series at different timescales by comparing a given peak candidate with a variable number of adjacent bins. While this has originally been conceived for the analysis of gamma-ray burst light (GRB) curves, its usage can be readily extended to other astrophysical transient phenomena whose activity is recorded through different surveys. MEPSA's high flexibility permits the mask of excess patterns it uses to be tailored and optimized without modifying the code.
megaman is a scalable manifold learning package implemented in python. It has a front-end API designed to be familiar to scikit-learn but harnesses the C++ Fast Library for Approximate Nearest Neighbors (FLANN) and the Sparse Symmetric Positive Definite (SSPD) solver Locally Optimal Block Precodition Gradient (LOBPCG) method to scale manifold learning algorithms to large data sets. It is designed for researchers and as such caches intermediary steps and indices to allow for fast re-computation with new parameters.
MegaLUT is a simple and fast method to correct ellipticity measurements of galaxies from the distortion by the instrumental and atmospheric point spread function (PSF), in view of weak lensing shear measurements. The method performs a classification of galaxies and associated PSFs according to measured shape parameters, and builds a lookup table of ellipticity corrections by supervised learning. This new method has been applied to the GREAT10 image analysis challenge, and demonstrates a refined solution that obtains the highly competitive quality factor of Q = 142, without any power spectrum denoising or training. Of particular interest is the efficiency of the method, with a processing time below 3 ms per galaxy on an ordinary CPU.
The Medium Energy Gamma-ray Astronomy library (MEGAlib) simulates, calibrates, and analyzes data of hard X-ray and gamma-ray detectors, with a specialization on Compton telescopes. The library comprises all necessary data analysis steps for these telescopes, from simulation/measurements via calibration, event reconstruction to image reconstruction.
MEGAlib contains a geometry and detector description tool for the detailed modeling of different detector types and characteristics, and provides an easy to use simulation program based on Geant4 (ascl:1010.079). For different Compton telescope detector types (electron tracking, multiple Compton or time of flight based), specialized Compton event reconstruction algorithms are implemented in different approaches (Chi-square and Bayesian). The high level data analysis tools calculate response matrices, perform image deconvolution (specialized in list-mode-likelihood-based Compton image reconstruction), determine detector resolutions and sensitivities, retrieve spectra, and determine polarization modulations.
We describe an automated method for assigning the most probable physical parameters to the components of an eclipsing binary, using only its photometric light curve and combined colors. With traditional methods, one attempts to optimize a multi-parameter model over many iterations, so as to minimize the chi-squared value. We suggest an alternative method, where one selects pairs of coeval stars from a set of theoretical stellar models, and compares their simulated light curves and combined colors with the observations. This approach greatly reduces the parameter space over which one needs to search, and allows one to estimate the components' masses, radii and absolute magnitudes, without spectroscopic data. We have implemented this method in an automated program using published theoretical isochrones and limb-darkening coefficients. Since it is easy to automate, this method lends itself to systematic analyses of datasets consisting of photometric time series of large numbers of stars, such as those produced by OGLE, MACHO, TrES, HAT, and many others surveys.
The Mechanic package is a numerical framework for dynamical astronomy, designed to help in massive numerical simulations by efficient task management and unified data storage. The code is built on top of the Message Passing Interface (MPI) and Hierarchical Data Format (HDF5) standards and uses the Task Farm approach to manage numerical tasks. It relies on the core-module approach. The numerical problem implemented in the user-supplied module is separated from the host code (core). The core is designed to handle basic setup, data storage and communication between nodes in a computing pool. It has been tested on large CPU-clusters, as well as desktop computers. The Mechanic may be used in computing dynamical maps, data optimization or numerical integration.
ME(SSY)**2 stands for “Monte-carlo Experiments with Spherically SYmmetric Stellar SYstems." This code simulates the long term evolution of spherical clusters of stars; it was devised specifically to treat dense galactic nuclei. It is based on the pioneering Monte Carlo scheme proposed by Hénon in the 70's and includes all relevant physical ingredients (2-body relaxation, stellar mass spectrum, collisions, tidal disruption, ldots). It is basically a Monte Carlo resolution of the Fokker-Planck equation. It can cope with any stellar mass spectrum or velocity distribution. Being a particle-based method, it also allows one to take stellar collisions into account in a very realistic way. This unique code, featuring most important physical processes, allows million particle simulations, spanning a Hubble time, in a few CPU days on standard personal computers and provides a wealth of data only rivalized by N-body simulations. The current version of the software requires the use of routines from the "Numerical Recipes in Fortran 77" (http://www.nrbook.com/a/bookfpdf.php).
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.
MCSED models the optical, near-infrared and infrared spectral energy distribution (SED) of galactic systems. Its modularity and options make it flexible and able to address the varying physical properties of galaxies over cosmic time and environment and adjust to changes in understanding of stellar evolution, the details of mass loss, and the products of binary evolution through substitution or addition of new datasets or algorithms. MCSED is built to fit a galaxy’s full SED, from the far-UV to the far-IR. Among other physical processes, it can model continuum emission from stars, continuum and line-emission from ionized gas, attenuation from dust, and mid- and far-IR emission from dust and polycyclic aromatic hydrocarbons (PAHs). MCSED performs its calculations by creating a complex stellar population (CSP) out of a linear combination of simple-stellar populations (SSPs) using an efficient Markov Chain Monte Carlo algorithm. It is very quick, and takes advantage of parallel processing.
McScatter illustrates a method of combining stellar dynamics with stellar evolution. The method is intended for elaborate applications, especially the dynamical evolution of rich star clusters. The dynamics is based on binary scattering in a multi-mass field of stars with uniform density and velocity dispersion, using the scattering cross section of Giersz (MNRAS, 2001, 324, 218-30).
MCRGNet (Morphological Classification of Radio Galaxy Network) classifies radio galaxies of different morphologies. It is based on the Convolutional Neural Network (CNN), which is trained and applied under a three-step framework: 1.) pretraining the network unsupervisedly with unlabeled samples, 2.) fine-tuning the pretrained network parameters supervisedly with labeled samples, and 3.) classifying a new radio galaxy by the trained network. The code uses a dichotomous tree classifier composed of cascaded CNN based subclassifiers.
MCRaT (Monte Carlo Radiation Transfer) analyzes the radiation signature expected from astrophysical outflows. MCRaT injects photons in a FLASH (ascl:1010.082) simulation and individually propagates and compton scatters the photons through the fluid until the end of the simulation. This process of injection and propagating occurs for a user specified number of times until there are no more photons to be injected. Users can then construct light curves and spectra with the MCRaT calculated results. The hydrodynamic simulations used with this version of MCRaT must be in 2D; however, the photon propagation and scattering is done in 3D by assuming cylindrical symmetry. Additionally, MCRaT uses the full Klein–Nishina cross section including the effects of polarization, which can be fully simulated in the code. MCRaT works with FLASH hydrodynamic simulations and PLUTO (ascl:1010.045) AMR simulations, with both 2D spherical (r, equation) and 2D cartesian ((x,y) and (r,z)).
The McGill Planar Hydrogen Atmosphere Code (McPHAC) v1.1 calculates the hydrostatic equilibrium structure and emergent spectrum of an unmagnetized hydrogen atmosphere in the plane-parallel approximation at surface gravities appropriate for neutron stars. McPHAC incorporates several improvements over previous codes for which tabulated model spectra are available: (1) Thomson scattering is treated anisotropically, which is shown to result in a 0.2%-3% correction in the emergent spectral flux across the 0.1-5 keV passband; (2) the McPHAC source code is made available to the community, allowing it to be scrutinized and modified by other researchers wishing to study or extend its capabilities; and (3) the numerical uncertainty resulting from the discrete and iterative solution is studied as a function of photon energy, indicating that McPHAC is capable of producing spectra with numerical uncertainties <0.01%. The accuracy of the spectra may at present be limited to ~1%, but McPHAC enables researchers to study the impact of uncertain inputs and additional physical effects, thereby supporting future efforts to reduce those inaccuracies. Comparison of McPHAC results with spectra from one of the previous model atmosphere codes (NSA) shows agreement to lsim1% near the peaks of the emergent spectra. However, in the Wien tail a significant deficit of flux in the spectra of the previous model is revealed, determined to be due to the previous work not considering large enough optical depths at the highest photon frequencies. The deficit is most significant for spectra with T eff < 105.6 K, though even there it may not be of much practical importance for most observations.
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