Results 1801-1850 of 3572 (3481 ASCL, 91 submitted)

[ascl:1306.010]
MADCOW: Microwave Anisotropy Dataset Computational softWare

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.

[ascl:2302.019]
MADCUBA: MAdrid Data CUBe Analysis

MADCUBA analyzes astronomical datacubes and multiple spectra from various astronomical facilities, including ALMA, Herschel, VLA, IRAM 30m, APEX, GBT, and others. These telescopes, and in particular ALMA, generate extremely large datacubes (spatial, spectral and polarization). This software combines a user-friendly interface and powerful data analysis system to derive the physical conditions of molecular gas, its chemical complexity and the kinematics from datacubes. Built using the ImageJ (ascl:1206.013) infrastructure, MADCUBA visualizes astronomical datacubes with thousands on spectral channels, and datasets with thousands of spectra; it also identifies molecular species using publicly available molecular catalogs. It can automatically derive the physical parameters of the molecular species: column density, excitation temperature, velocity and linewidths and provides the best non-linear least-squared fit using the Levenberg-Marquardt algorithm, among other tasks.

[ascl:1712.012]
MadDM: Computation of dark matter relic abundance

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.

[ascl:2009.009]
MADHAT: Gamma-ray emission analyzer

Boddy, Kimberly K.; Hill, Stephen; Kumar, Jason; Sandick, Pearl; Haghi, Barmak Shams Es; Marfatia, Danny

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.

[ascl:2012.010]
MADLens: Differentiable lensing simulator

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.

[ascl:1110.018]
MADmap: Fast Parallel Maximum Likelihood CMB Map Making Code

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.

[ascl:2206.018]
MADYS: Isochronal parameter determination for young stellar and substellar objects

MADYS (Manifold Age Determination for Young Stars) determines the age and mass of young stellar and substellar objects. The code automatically retrieves and cross-matches photometry from several catalogs, estimates interstellar extinction, and derives age and mass estimates for individual objects through isochronal fitting. MADYS harmonizes the heterogeneity of publicly-available isochrone grids and the user can choose amongst several models, some of which have customizable astrophysical parameters. Particular attention has been dedicated to the categorization of these models, labeled through a four-level taxonomical classification.

[ascl:2205.005]
maelstrom: Forward modeling of pulsating stars in binaries

Hey, Daniel R.; Murphy, Simon J.; Foreman-Mackey, Dan; Bedding, Timothy R.; Pope, Benjamin J.S.; Hogg, David W.

maelstrom models binary orbits through the phase modulation technique. This set of custom PyMC3 models and solvers fit each individual datapoint in the time series by forward modeling the time delay onto the light curve. This approach fully captures variations in a light curve caused by an orbital companion.

[ascl:1010.044]
MAESTRO: An Adaptive Low Mach Number Hydrodynamics Algorithm for Stellar Flows

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.

[ascl:1908.019]
MAESTROeX: Low Mach number stellar hydrodynamics code

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.

[ascl:2007.015]
MAGI: Initial-condition generator for galactic N-body simulations

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.

[ascl:1709.010]
MagIC: Fluid dynamics in a spherical shell simulator

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.

[ascl:1604.004]
magicaxis: Pretty scientific plotting with minor-tick and log minor-tick support

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.

[ascl:1303.009]
MAGIX: Modeling and Analysis Generic Interface for eXternal numerical codes

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.

[ascl:2003.002]
MAGNETAR: Histogram of relative orientation calculator for MHD observations

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).

[ascl:1010.054]
MagnetiCS.c: Cosmic String Loop Evolution and Magnetogenesis

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.

[ascl:2008.011]
Magnetizer: Computing magnetic fields of evolving galaxies

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.

[ascl:1502.014]
Magnetron: Fitting bursts from magnetars

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.

[ascl:1106.010]
MAGPHYS: Multi-wavelength Analysis of Galaxy Physical Properties

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.

[ascl:2310.006]
MAGPy-RV: Gaussian Process regression pipeline with MCMC parameter searching

MAGPy-RV (Modelling stellar Activity with Gaussian Processes in Radial Velocity) models data with Gaussian Process regression and affine invariant Monte Carlo Markov Chain parameter searching. Developed to model intrinsic, quasi-periodic variations induced by the host star in radial velocity (RV) surveys for the detection of exoplanets and the accurate measurements of their orbital parameters and masses, it now includes a variety of kernels and models and can be applied to any timeseries analysis. MAGPy-RV includes publication level plotting, efficient posterior extraction, and export-ready LaTeX results tables. It also handles multiple datasets at once and can model offsets and systematics from multiple instruments. MAGPy-RV requires no external dependencies besides basic python libraries and corner (ascl:1702.002).

[ascl:2203.024]
Magrathea-Pathfinder: 3D AMR ray-tracing in simulations

Magrathea-Pathfinder propagates photons within cosmological simulations to construct observables. This high-performance framework uses a 3D Adaptive-Mesh Refinement and is built on top of the MAGRATHEA metalibrary (ascl:2203.023).

[ascl:2203.023]
MAGRATHEA: Multi-processor Adaptive Grid Refinement Analysis for THEoretical Astrophysics

MAGRATHEA (Multi-processor Adaptive Grid Refinement Analysis for THEoretical Astrophysics) is a foundational cosmological library and a relativistic raytracing code. Classical linear algebra libraries come with their own operations and can be difficult to leverage for new data types. Instead of providing basic types, MAGRATHEA provides tools to generate base types such as scalar quantities, points, vectors, or tensors.

[ascl:2201.012]
MAGRATHEA: Planet interior structure code

MAGRATHEA solves planet interiors and considers the case of fully differentiated interiors. The code integrates the hydrostatic equation in order to determine the correct planet radius given the mass in each layer. The code returns the pressure, temperature, density, phase, and radius at steps of enclosed mass. The code support four layers: core, mantle, hydrosphere, and atmosphere. Each layer has a phase diagram with equations of state chosen for each phase.

[ascl:2012.025]
Magritte: 3D radiative transfer library

De Ceuster, Frederik; Bolte, Jan; Homan, Ward; Maes, Silke; Malfait, Jolien; Decin, Leen; Yates, Jeremy; Boyle, Peter; Hetherington, James

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.

[ascl:1307.009]
MAH: Minimum Atmospheric Height

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.

[ascl:2106.011]
MakeCloud: Turbulent GMC initial conditions for GIZMO

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.

[ascl:2407.001]
MAKEE: MAuna Kea Echelle Extraction

MAKEE (MAuna Kea Echelle Extraction) reduces data from the HIRES and ESI instruments at Keck Observatory. It is optimized for the spectral extraction of single, unresolved point sources and is designed to run non-interactively using a set of default parameters. Taking the raw HIRES FITS files as input, the code determines the position (or trace) of each echelle order, defines the object and background extraction boundaries, optimally extracts a spectrum for each order, and computes wavelength calibrations. MAKEE produces FITS format "spectral images" (each row is a separate echelle order spectrum) and the data values are in arbitrary (relative) flux units. MAKEE will reduce data from all HIRES formats, including the single CCD format, the single CCD with Red and UV cross dispersers, and the current 3 CCD system. It can handle a variety of pixel binnings, including 1x1, 1x2, 1x4 (column x row).

[ascl:1502.021]
MaLTPyNT: Quick look timing analysis for NuSTAR data

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.

[submitted]
MALU IFS visualisation tool

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.

[ascl:2203.020]
MAMPOSSt: Mass/orbit modeling of spherical systems

MAMPOSSt (Modeling Anisotropy and Mass Profiles of Observed Spherical Systems) is a Bayesian code to perform mass/orbit modeling of spherical systems. It determines marginal parameter distributions and parameter covariances of parametrized radial distributions of dark or total matter, as well as the mass of a possible central black hole, and the radial profiles of density and velocity anisotropy of one or several tracer components, all of which are jointly fit to the discrete data in projected phase space. It is based upon the MAMPOSSt likelihood function for the distribution of individual tracers in projected phase space (projected radius and line-of-sight velocity) and the CosmoMC Markov Chain Monte Carlo code (ascl:1106.025), run in generic mode. MAMPOSSt is not based on the 6D distribution function (which would require triple integrals), but on the assumption that the local 3D velocity distribution is an (anisotropic) Gaussian (requiring only a single integral).

[ascl:2106.010]
Maneage: Managing data lineage

Akhlaghi, Mohammad; Infante-Sainz, Raúl; Roukema, Boudewijn; Khellat, Mohammadreza; Valls-Gabaud, David; Baena-Gallé, Roberto

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.

[ascl:2203.017]
MaNGA-DAP: MaNGA Data Analysis Pipeline

Westfall, Kyle B.; Cappellari, Michele; Bershady, Matthew A.; Bundy, Kevin; Belfiore, Francesco; Ji, Xihan; Law, David R.; Schaefer, Adam; Shetty, Shravan; Tremonti, Christy A.; Yan, Renbin; Andrews, Brett H.; Brownstein, Joel R.; Cherinka, Brian; Coccato, Lodovico; Drory, Niv; Maraston, Claudia; Parikh, Taniya; Sánchez-Gallego, José R.; Thomas, Daniel; Weijmans, Anne-Marie; Barrera-Ballesteros, Jorge; Du, Cheng; Goddard, Daniel; Li, Niu; Masters, Karen; Ibarra Medel, Héctor Javier; Sánchez, Sebastián F.; Yang, Meng; Zheng, Zheng; Zhou, Shuang

The MaNGA data analysis pipeline (MaNGA DAP) analyzes the data produced by the MaNGA data-reduction pipeline (ascl:2203.016) to produced physical properties derived from the MaNGA spectroscopy. All survey-provided properties are currently derived from the log-linear binned datacubes (*i.e.*, the LOGCUBE files).

[ascl:2203.016]
MaNGA-DRP: MaNGA Data Reduction Pipeline

Law, David R.; Cherinka, Brian; Yan, Renbin; Andrews, Brett H.; Bershady, Matthew A.; Bizyaev, Dmitry; Blanc, Guillermo A.; Blanton, Michael R.; Bolton, Adam S.; Brownstein, Joel R.; Bundy, Kevin; Chen, Yanmei; Drory, Niv; D'Souza, Richard; Fu, Hai; Jones, Amy; Kauffmann, Guinevere; MacDonald, Nicholas; Masters, Karen L.; Newman, Jeffrey A.; Parejko, John K.; Sánchez-Gallego, José R.; Sánchez, Sebastian F.; Schlegel, David J.; Thomas, Daniel; Wake, David A.; Weijmans, Anne-Marie; Westfall, Kyle B.; Zhang, Kai

The MaNGA Data Reduction Pipeline (DRP) processes the raw data to produce flux calibrated, sky subtracted, coadded data cubes from each of the individual exposures for a given galaxy. The DRP consists of two primary parts: the 2d stage that produces flux calibrated fiber spectra from raw individual exposures, and the 3d stage that combines multiple flux calibrated exposures with astrometric information to produce stacked data cubes. These science-grade data cubes are then processed by the MaNGA Data Analysis Pipeline (ascl:2203.017), which measures the shape and location of various spectral features, fits stellar population models, and performs a variety of other analyses necessary to derive astrophysically meaningful quantities from the calibrated data cubes.

[ascl:1202.005]
Mangle: Angular Mask Software

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.

[ascl:2306.015]
Mangrove: Infer galaxy properties using dark matter merger trees

Jespersen, Christian Kragh; Cranmer, Miles; Melchior, Peter; Ho, Shirley; Somerville, Rachel S.; Gabrielpillai, Austen

Mangrove uses Graph Neural Networks to regress baryonic properties directly from full dark matter merger trees to infer galaxy properties. The package includes code for preprocessing the merger tree, and training the model can be done either as single experiments or as a sweep. Mangrove provides loss functions, learning rate schedulers, models, and a script for doing the training on a GPU.

[ascl:1305.012]
MapCUMBA: Multi-grid map-making algorithm for CMB experiments

The MapCUMBA package applies a multigrid fast iterative Jacobi algorithm for map-making in the context of CMB experiments.

[ascl:1308.003]
MapCurvature: Map Projections

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.

[ascl:1306.008]
MAPPINGS III: Modelling And Prediction in PhotoIonized Nebulae and Gasdynamical Shocks

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. A newer version of this software, MAPPINGS V (ascl:1807.005), is available.

[ascl:1807.005]
MAPPINGS V: Astrophysical plasma modeling code

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.

[ascl:2108.003]
MAPS: Multi-frequency Angular Power Spectrum estimator

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.

[ascl:2306.011]
margarine: Posterior sampling and marginal Bayesian statistics

Margarine computes marginal bayesian statistics given a set of samples from an MCMC or nested sampling run. Specifically, the code calculates marginal Kullback-Leibler divergences and Bayesian dimensionalities using Masked Autoregressive Flows and Kernel Density Estimators to learn and sample posterior distributions of signal subspaces in high dimensional data models, and determines the properties of cosmological subspaces, such as their log-probability densities and how well constrained they are, independent of nuisance parameters. Margarine thus allows for direct and specific comparison of the constraining ability of different experimental approaches, which can in turn lead to improvements in experimental design.

[ascl:2003.010]
MARGE: Machine learning Algorithm for Radiative transfer of Generated Exoplanets

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 (MC^{3}, 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.

[ascl:1011.004]
MARS: The MAGIC Analysis and Reconstruction Software

Moralejo, R. A.; Gaug, M.; Carmona, E.; Colin, P.; Delgado, C.; Lombardi, S.; Mazin, D.; Scalzotto, V.; Sitarek, J.; Tescaro, D.

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.

[ascl:1910.015]
MarsLux: Illumination Mars maps generator

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.

[ascl:1911.005]
MARTINI: Mock spatially resolved spectral line observations of simulated galaxies

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.

[ascl:2106.005]
Marvin: Data access and visualization for MaNGA

Cherinka, Brian; Andrews, Brett H.; Sánchez-Gallego, José; Brownstein, Joel; Argudo-Fernández, María; Blanton, Michael; Bundy, Kevin; Jones, Amy; Masters, Karen; Law, David R.; Rowlands, Kate; Weijmans, Anne-Marie; Westfall, Kyle; Yan, Renbin

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.

[ascl:1302.001]
MARX: Model of AXAF Response to X-rays

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.

[ascl:1711.020]
MARXS: Multi-Architecture Raytrace Xray mission Simulator

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.

[ascl:1605.001]
MARZ: Redshifting Program

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.

[ascl:2101.007]
Mask galaxy: Machine learning pipeline for morphological segmentation of galaxies

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.

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