MultiModeCode facilitates efficient Monte Carlo sampling of prior probabilities for inflationary model parameters and initial conditions and efficiently generates large sample-sets for inflation models with O(100) fields. The code numerically solves the equations of motion for the background and first-order perturbations of multi-field inflation models with canonical kinetic terms and arbitrary potentials, providing the adiabatic, isocurvature, and tensor power spectra at the end of inflation. For models with sum-separable potentials MultiModeCode also computes the slow-roll prediction via the δN formalism for easy model exploration and validation.

MULTIGRIS (also called mgris) uses the sequential Monte Carlo method in PyMC (ascl:1506.005) to extract the posterior distributions of primary grid parameters and predict unobserved parameters/observables. The code accepts either a discrete number of components and/or continuous (e.g., power-law, normal) distributions for any given parameter. MULTIGRIS, written in Python, infers the posterior probability functions of parameters in a multidimensional potentially incomplete grid with some observational tracers defined for each parameter set. Observed values and their potentially asymmetric uncertainties are used to calculate a likelihood which, together with predefined or custom priors, produces the posterior distributions. Linear combinations of parameter sets may be used with inferred mixing weights and nearest neighbor or linear interpolation may be used to sample the parameter space.

MultiColorFits is a tool to colorize and combine multiple fits images for making visually aesthetic scientific plots. The standard method to make color composites by combining fits images programmatically in python is to assign three images as separate red, green, and blue channels. This can produce unsatisfactory results for a variety of reasons, such as when less than three images are available, or additional images are desired to be shown. MultiColorFits breaks these limitations by allowing users to apply any color to a given image, not just red, green, or blue. Composites can then be created from an arbitrary number of images. Controls are included for stretching brightness scales with common functions.

The multiband periodogram is a general extension of the well-known Lomb-Scargle approach for detecting periodic signals in time-domain data. In addition to advantages of the Lomb-Scargle method such as treatment of non-uniform sampling and heteroscedastic errors, the multiband periodogram significantly improves period finding for randomly sampled multiband light curves (e.g., Pan-STARRS, DES and LSST). The light curves in each band are modeled as arbitrary truncated Fourier series, with the period and phase shared across all bands.

Multi_CLASS modifies the Boltzmann code CLASS (ascl:1106.020) to compute of the cross-tracer angular power spectra of the number count fluctuations for two different tracers of the underlying dark matter density field. In other words, it generalizes the standard nCl output option of CLASS to the case of two different tracers, for example, two different galaxy populations with their own redshift distribution, and galaxy and magnification bias parameters, among others.

Multi_CLASS also includes an implementation of the effect of primordial non-Gaussianities of the local type, parametrized by the parameter f_NL (following the large-scale structure convention), on the effective bias of the tracers. There is also the possibility of having a tilted non-Gaussian correction, parametrized by n_NG, with a pivot scale determined by k_pivot_NG. The package includes galaxy redshift distributions for forthcoming galaxy surveys, with the ease of choosing between them (or an input file) from the parameters input file (e.g., multi_explanatory.ini). In addition, Multi_CLASS includes the possibility of using resolved gravitational wave events as a tracer.

MulensModel calculates light curves of microlensing events. Both single and binary lens events are modeled and various higher-order effects can be included: extended source (with limb-darkening), annual microlensing parallax, and satellite microlensing parallax. The code is object-oriented and written in Python3, and requires AstroPy (ascl:1304.002).

muLAn analyzes and fits light curves of gravitational microlensing events. The code includes all classical microlensing models (for example, single and binary microlenses, ground- and space-based parallax effects, orbital motion, finite-source effects, and limb-darkening); these can be combined into several time intervals of the analyzed light curve. Minimization methods include an Affine-Invariant Ensemble Sampler to generate a multivariate proposal function while running several Markov Chain Monte Carlo (MCMC) chains, for the set of parameters which is chosen to be fit; non-fitting parameters can be either kept fixed or set on a grid defined by the user. Furthermore, the software offers a model-free option to align all data sets together and allow inspection the light curve before any modeling work. It also comes with many useful routines (export publication-quality figures, data formatting and cleaning) and state-of-the-art statistical tools.

Modeling results can be interpreted using an interactive html page which contains all information about the light curve model, caustics, source trajectory, best-fit parameters and chi-square. Parameters uncertainties and statistical properties (such as multi-modal features of the posterior density) can be assessed from correlation plots. The code is modular, allowing the addition of other computation or minimization routines by directly adding their Python files without modifying the main code. The software has been designed to be easy to use even for the newcomer in microlensing, with external, synthetic and self-explanatory setup files containing all important commands and option settings. The user may choose to launch the code through command line instructions, or to import muLAn within another Python project like any standard Python package.

mTransport computes the 2-point-correlation function of the curvature and tensor perturbations in multifield models of inflation in the presence of a curved field space. It is a Mathematica implementation of the transport method which encompasses scenarios with violations of slow-roll conditions and turns of the trajectory in field space. It can be used for an arbitrary mass spectrum, including massive modes, particle production and models with quasi-single-field dynamics.

MTNeedlet uses needlets to filter spherical (Healpix) maps and detect and analyze the maxima population using a multiple testing approach. It has been developed with the CMB in mind, but it can be applied to other spherical maps. It pivots around three basic steps: 1.) The calculation of several types of needlets and their possible use to filter maps; 2.) The detection of maxima (or minima) on spherical maps, their visualization and basic analysis; and 3.) The multiple testing approach in order to detect anomalies in the maxima population of the maps with respect to the expected behavior for a random Gaussian map. MTNeedlet relies on Healpy (ascl:2008.022) to efficiently deal with spherical maps.

MSWAVEF calculates hydrogenic and non-hydrogenic momentum-space electronic wavefunctions. Such wavefunctions are often required to calculate various collision processes, such as excitation and line broadening cross sections. The hydrogenic functions are calculated using the standard analytical expressions. The non-hydrogenic functions are calculated within quantum defect theory according to the method of Hoang Binh and van Regemorter (1997). Required Hankel transforms have been determined analytically for angular momentum quantum numbers ranging from zero to 13 using Mathematica. Calculations for higher angular momentum quantum numbers are possible, but slow (since calculated numerically). The code is written in IDL.

MST (Minimum Spanning Tree) identifies velocity coherent large-scale filaments through ATLASGAL clumps. It can also isolate filaments embedded in a crowded position–position–velocity (PPV) space. One strength of this method is its repeatability compared to manual approaches.

Multi-Source Self-Calibration (MSSC) provides direction-dependent calibration to standard phase referencing. The code combines multiple faint sources detected within the primary beam to derive phase corrections. Each source has its CLEAN model divided into the visibilities which results in multiple point sources that are stacked in the uv plane to increase the S/N, thus permitting self-calibration. This process applies only to wide-field VLBI data sets that detect and image multiple sources within one epoch.

MSL applies simulation-based inference techniques to the problem of substructure inference in galaxy-galaxy strong lenses. It leverages additional information extracted from the simulator, then trains neural networks to estimate likelihood ratios associated with population-level parameters characterizing dark matter substructure. The package including five high-level scripts which run the simulation and create samples, combing multiple simulation runs into a single file to use for training, then train the neural networks. After training, the estimated likelihood ratio is tested, and calibrated network predictions are made based on histograms of the network output.

Future cosmological surveys will provide 3D large scale structure maps with large sky coverage, for which a 3D Spherical Fourier-Bessel (SFB) analysis is natural. Wavelets are particularly well-suited to the analysis and denoising of cosmological data, but a spherical 3D isotropic wavelet transform does not currently exist to analyse spherical 3D data. We present a new fast Discrete Spherical Fourier-Bessel Transform (DSFBT) based on both a discrete Bessel Transform and the HEALPIX angular pixelisation scheme. We tested the 3D wavelet transform and as a toy-application, applied a denoising algorithm in wavelet space to the Virgo large box cosmological simulations and found we can successfully remove noise without much loss to the large scale structure. The new spherical 3D isotropic wavelet transform, called MRS3D, is ideally suited to analysing and denoising future 3D spherical cosmological surveys; it uses a novel discrete spherical Fourier-Bessel Transform. MRS3D is based on two packages, IDL and Healpix and can be used only if these two packages have been installed.

The MRS (The MOS Reduction Software) suite reduces the spectra taken with the multi-object spectrograph spectra used as the focal plane instrument of RTT150 telescope in the TÜBİTAK National Observatory.

MRrelation calculates the posterior predictive mass distribution for an individual planet. The probabilistic mass-radius relationship (M-R relation) is evaluated within a Bayesian framework, which both quantifies this intrinsic dispersion and the uncertainties on the M-R relation parameters.

mrpy calculates the MRP parameterization of the Halo Mass Function. It calculates basic statistics of the truncated generalized gamma distribution (TGGD) with the TGGD class, including mean, mode, variance, skewness, pdf, and cdf. It generates MRP quantities with the MRP class, such as differential number counts and cumulative number counts, and offers various methods for generating normalizations. It can generate the MRP-based halo mass function as a function of physical parameters via the mrp_b13 function, and fit MRP parameters to data in the form of arbitrary curves and in the form of a sample of variates with the SimFit class. mrpy also calculates analytic hessians and jacobians at any point, and allows the user to alternate parameterizations of the same form via the reparameterize module.

MrMoose (Multi-Resolution Multi-Object/Origin Spectral Energy) fits user-defined models onto a set of multi-wavelength data using a Bayesian framework. The code can handle blended sources, large variation in resolution, and even upper limits consistently. It also generates a series of outputs allowing for an quick interpretation of the results. The code uses emcee (ascl:1303.002), and saves the emcee sampler object, thus allowing users to transfer the output to a personal graphical interface.

The MRLENS package offers a new method for the reconstruction of weak lensing mass maps. It uses the multiscale entropy concept, which is based on wavelets, and the False Discovery Rate which allows us to derive robust detection levels in wavelet space. We show that this new restoration approach outperforms several standard techniques currently used for weak shear mass reconstruction. This method can also be used to separate E and B modes in the shear field, and thus test for the presence of residual systematic effects. We concentrate on large blind cosmic shear surveys, and illustrate our results using simulated shear maps derived from N-Body Lambda-CDM simulations with added noise corresponding to both ground-based and space-based observations.

MRExo performs non-parametric fitting and analysis of the mass-radius (M-R) relationship for exoplanets. Written in Python, it offers tools for fitting the M-R relationship to a given data set and also includes predicting (M->R, and R->M) and plotting functions.

MPWide is a light-weight communication library for distributed computing. It is specifically developed to allow message passing over long-distance networks using path-specific optimizations. An early version of MPWide was used in the Gravitational Billion Body Project to allow simulations across multiple supercomputers.

MPSolve (Multiprecision Polynomial SOLVEr) provides an easy-to-use universal blackbox for solving polynomials and secular equations. Its features include arbitrary precision approximation and guaranteed inclusion radii for the results. It can exploit polynomial structures, taking advantage of sparsity as well as coefficients in a particular domain (i.e., integers or rationals), and can be specialized for specific classes of polynomials.

MPI-Defrost extends Frolov’s Defrost (ascl:1011.012) to an MPI-based cluster environment. This version has been restricted to a single field. Restoring two-field support should be straightforward, but will require some code changes. Some output options may also not be fully supported under MPI.

This code was produced to support our own work, and has been made available for the benefit of anyone interested in either oscillon simulations or an MPI capable version of Defrost, and it is provided on an "as-is" basis. Andrei Frolov is the primary developer of Defrost and we thank him for placing his work under the GPL (GNU Public License), and thus allowing us to distribute this modified version.

MPI-AMRVAC is an MPI-parallelized Adaptive Mesh Refinement code, with some heritage (in the solver part) to the Versatile Advection Code or VAC, initiated by Gábor Tóth at the Astronomical Institute at Utrecht in November 1994, with help from Rony Keppens since 1996. Previous incarnations of the Adaptive Mesh Refinement version of VAC were of restricted use only, and have been used for basic research in AMR strategies, or for well-targeted applications. This MPI version uses a full octree block-based approach, and allows for general orthogonal coordinate systems. MPI-AMRVAC aims to advance any system of (primarily hyperbolic) partial differential equations by a number of different numerical schemes. The emphasis is on (near) conservation laws, with shock-dominated problems as a main research target. The actual equations are stored in separate modules, can be added if needed, and they can be selected by a simple configuration of the VACPP preprocessor. The dimensionality of the problem is also set through VACPP. The numerical schemes are able to handle discontinuities and smooth flows as well.

MPI_XSTAR parallelizes execution of multiple XSTAR runs using Message Passing Interface (MPI). XSTAR (ascl:9910.008), part of the HEASARC's HEAsoft (ascl:1408.004) package, calculates the physical conditions and emission spectra of ionized gases. MPI_XSTAR invokes XSTINITABLE from HEASoft to generate a job list of XSTAR commands for given physical parameters. The job list is used to make directories in ascending order, where each individual XSTAR is spawned on each processor and outputs are saved. HEASoft's XSTAR2TABLE program is invoked upon the contents of each directory in order to produce table model FITS files for spectroscopy analysis tools.

MPgrafic is a parallel MPI version of Grafic-1 (ascl:9910.004) which can produce large cosmological initial conditions on a cluster without requiring shared memory. The real Fourier transforms are carried in place using fftw while minimizing the amount of used memory (at the expense of performance) in the spirit of Grafic-1. The writing of the output file is also carried in parallel. In addition to the technical parallelization, it provides three extensions over Grafic-1:

• it can produce power spectra with baryon wiggles (DJ Eisenstein and W. Hu, Ap. J. 496);
• it has the optional ability to load a lower resolution noise map corresponding to the low frequency component which will fix the larger scale modes of the simulation (extra flag 0/1 at the end of the input process) in the spirit of Grafic-2 (ascl:1106.008);
• it can be used in conjunction with constrfield, which generates initial conditions phases from a list of local constraints on density, tidal field density gradient and velocity.

These IDL routines provide a robust and relatively fast way to perform least-squares curve and surface fitting. The algorithms are translated from MINPACK-1, which is a rugged minimization routine found on Netlib, and distributed with permission. This algorithm is more desirable than CURVEFIT because it is generally more stable and less likely to crash than the brute-force approach taken by CURVEFIT, which is based upon Numerical Recipes.

MPDAF, the MUSE Python Data Analysis Framework, provides tools to work with MUSE-specific data (for example, raw data and pixel tables), and with more general data such as spectra, images, and data cubes. Originally written to work with MUSE data, it can also be used for other data, such as that from the Hubble Space Telescope. MPDAF also provides MUSELET, a SExtractor-based tool to detect emission lines in a data cube, and a format to gather all the information on a source in one FITS file. MPDAF was developed and is maintained by CRAL (Centre de Recherche Astrophysique de Lyon).

MOSFiT (Modular Open-Source Fitter for Transients) downloads transient datasets from open online catalogs (e.g., the Open Supernova Catalog), generates Monte Carlo ensembles of semi-analytical light curve fits to those datasets and their associated Bayesian parameter posteriors, and optionally delivers the fitting results back to those same catalogs to make them available to the rest of the community. MOSFiT helps bridge the gap between observations and theory in time-domain astronomy; in addition to making the application of existing models and creation of new models as simple as possible, MOSFiT yields statistically robust predictions for transient characteristics, with a standard output format that includes all the setup information necessary to reproduce a given result.

MosfireDRP reduces data from the MOSFIRE spectrograph of the Keck Observatory; it produces flat-fielded, wavelength calibrated, rectified, and stacked 2D spectrograms for each slit on a given mask in nearly real time. Background subtraction is performed in two states: a simple pairwise subtraction of interleaved stacks, and then fitting a 2D b-spline model to the background residuals.

MOSAIC (Multipole Operators in Symbols, Automatically Improved and Condensed) automatically produces, verifies, and optimizes computer code for Fast Multipole Method (FMM) operators. It is based on a symbolic algebra library, and can produce code for any expansion order and be extended to use any basis or kernel function. The code applies algebraic modifications to reduce the number of floating-point operations and can symbolically verify correctness.

MORPHOFIT consists of a series of modules for estimating galaxy structural parameters. The package uses SEXTRACTOR (ascl:1010.064) in forced photometry mode to get an initial estimate of the galaxy structural parameters and create a multiband catalog. It also uses GALFIT (ascl:1010.064), running it on galaxy stamps and galaxy regions from the parent image and also on galaxies from the full image using SEXTRACTOR properties as input. MORPHOFIT has been optimized and tested in both low-density and crowded environments, and can recover the input structural parameters of galaxies with good accuracy.

Morpheus generates pixel level morphological classifications of astronomical sources by leveraging advances in deep learning to perform source detection, source segmentation, and morphological classification pixel-by-pixel via a semantic segmentation algorithm adopted from the field of computer vision. By utilizing morphological information about the flux of real astronomical sources during object detection, Morpheus shows resiliency to false positive identifications of sources.

MORPHEUS (Manchester Omni-geometRical Program for Hydrodynamical EUlerian Simulations) is a 3D hydrodynamical code used to simulate astrophysical fluid flows. It has three different grid geometries (cartesian, spherical, and cylindrical) and uses a second-order Godunov method to solve the equations of hydrodynamics. Physical modules also include radiative cooling and gravity, and a hybrid MPI-OpenMP parallelization allows computations to be run on large-scale architectures. MORPHEUS is written in Fortran90 and does not require any libraries (apart from MPI) to run.

MORDI (Massively-Overlapped Ring-Diagram Inversion) performs three-dimensional ring-diagram inversions. The code reads in frequency shift measurements and their associated sensitivity kernels and outputs two-dimensional slices of the subsurface flow field at a constant depth and (optionally) the associated averaging kernels. It relies on both distributed-memory (MPI) and shared-memory (OpenMP) parallelism to scale efficiently up to a few thousand processors, but can also run reasonably well on small machines (1-4 cpus). The actions of the code are modified by command-line parameters, which enable a significant amount of flexibility when setting up an inversion.

MOPSIC was created to analyze bolometer data but can be used for much more versatile tasks. It is an extension of MOPSI; this software had been merged with the command interpreter of GILDAS (ascl:1305.010). For data reduction, MOPSIC uses a special method to calculate the chopped signal. This gives much better results than the straight difference of the signals obtained at both chopper positions. In addition there are also scripts to reduce pointings, skydips, and to calculate the RCPs (Receiver Channel Parameters) from calibration maps. MOPSIC offers a much broader range of applications including advanced planning functions for mapping and onoff observations, post-reduction data analysis and processing and even reduction of non-bolometer data (optical, IR, spectroscopy).

MOPEX (MOsaicker and Point source EXtractor) is a package for reducing and analyzing imaging data, as well as MIPS SED data. MOPEX includes the point source extraction package, APEX.
MOPEX is designed to allow the user to:

• perform sophisticated background matching of individual data frames
• mosaic the individual frames downloaded from the Spitzer archive
• perform both temporal and spatial outlier rejection during mosaicking
• apply offline pointing refinement for MIPS data (refinement is already applied to IRAC data)
• perform source detection on the mosaics using APEX
• compute aperture photometry or PRF-fitting photometry for point sources
• perform interpolation, coaddition, and spectrum extraction of MIPS SED images.

MOOGStokes is a version of the MOOG one-dimensional local thermodynamic equilibrium radiative transfer code that incorporates a Stokes vector treatment of polarized radiation through a magnetic medium. It consists of three complementary programs that together can synthesize the disk-averaged emergent spectrum of a star with a magnetic field. The MOOGStokes package synthesizes emergent spectra of stars with magnetic fields in a familiar computational framework and produces disk-averaged spectra for all Stokes vectors ( I, Q, U, V ), normalized by the continuum.

MOOG performs a variety of LTE line analysis and spectrum synthesis tasks. The typical use of MOOG is to assist in the determination of the chemical composition of a star. The basic equations of LTE stellar line analysis are followed. The coding is in various subroutines that are called from a few driver routines; these routines are written in standard FORTRAN. The standard MOOG version has been developed on unix, linux and macintosh computers.

One of the chief assets of MOOG is its ability to do on-line graphics. The plotting commands are given within the FORTRAN code. MOOG uses the graphics package SM, chosen for its ease of implementation in FORTRAN codes. Plotting calls are concentrated in just a few routines, and it should be possible for users of other graphics packages to substitute other appropriate FORTRAN commands.

MOOG_SCAT, a redevelopment of the LTE radiative transfer code MOOG (ascl:1202.009), contains modifications that allow for the treatment of isotropic, coherent scattering in stars. MOOG_SCAT employs a modified form of the source function and solves radiative transfer with a short charactersitics approach and an acclerated lambda iteration scheme.

MontePython 3 provides numerous ways to explore parameter space using Monte Carlo Markov Chain (MCMC) sampling, including Metropolis-Hastings, Nested Sampling, Cosmo Hammer, and a Fisher sampling method. This improved version of the Monte Python (ascl:1307.002) parameter inference code for cosmology offers new ingredients that improve the performance of Metropolis-Hastings sampling, speeding up convergence and offering significant time improvement in difficult runs. Additional likelihoods and plotting options are available, as are post-processing algorithms such as Importance Sampling and Adding Derived Parameter.

Monte Python is a parameter inference code which combines the flexibility of the python language and the robustness of the cosmological code CLASS (ascl:1106.020) into a simple and easy to manipulate Monte Carlo Markov Chain code.

This version has been archived and replaced by MontePython 3 (ascl:1805.027).

Montblanc, written in Python, is a GPU implementation of the Radio interferometer measurement equation (RIME) in support of the Bayesian inference for radio observations (BIRO) technique. The parameter space that BIRO explores results in tens of thousands of computationally expensive RIME evaluations before reduction to a single X² value. The RIME is calculated over four dimensions, time, baseline, channel and source and the values in this 4D space can be independently calculated; therefore, the RIME is particularly amenable to a parallel implementation accelerated by Graphics Programming Units (GPUs). Montblanc is implemented for NVIDIA's CUDA architecture and outperforms MeqTrees (ascl:1209.010) and OSKAR.

Montage is an open source code toolkit for assembling Flexible Image Transport System (FITS) images into custom mosaics. It runs on all common Linux/Unix platforms, on desktops, clusters and computational grids, and supports all World Coordinate System (WCS) projections and common coordinate systems. Montage preserves spatial and calibration fidelity of input images, processes 40 million pixels in up to 32 minutes on 128 nodes on a Linux cluster, and provides independent engines for analyzing the geometry of images on the sky, re-projecting images, rectifying background emission to a common level, and co-adding images. It offers convenient tools for managing and manipulating large image files.

MonoTools detects, vets, and models transiting exoplanets, with a specific emphasis on monotransiting planets and those with unknown periods. It includes scripts specifically for searching and assessing a lightcurve for the presence of monotransits. MonoTools can also performing a best-fit transit model, determine whether transits are linked to any detected multi-transiting planet candidate or with each other, and can fit planets in a Bayesian way to account for uncertain periods, lightcurve gaps, and stellar variability, among other things.

MONDPMesh provides a particle-mesh method to calculate the time evolution of an system of point masses under modified gravity, namely the AQUAL formalism. This is done by transforming the Poisson equation for the potential into a system of four linear PDEs, and solving these using fast Fourier transforms. The accelerations on the point masses are calculated from this potential, and the system is propagated using Leapfrog integration. The time complexity of the code is O(N⋅p⋅log(p)) for p pixels and N particles, which is the same as for a Newtonian particle-mesh code.

Molsoft operates, monitors and schedules observations, both through predetermined schedule files and fully dynamically, at the refurbished Molonglo Observatory Synthesis Radio Telescope (MOST). It was developed as part of the UTMOST upgrade of the facility. The software runs a large-scale pulsar timing program; the autonomous observing system and the dynamic scheduler have increased the observing efficiency by a factor of 2-3 in comparison with static scheduling.

MOLSCAT version 14 is a FORTRAN code for quantum mechanical (coupled channel) solution of the nonreactive molecular scattering problem and was developed to obtain collision rates for molecules in the interstellar gas which are needed to understand microwave and infrared astronomical observations. The code is implemented for various types of collision partners. In addition to the essentially exact close coupling method several approximate methods, including the Coupled States and Infinite Order Sudden approximations, are provided. This version of the code has been superseded by MOLSCAT 2020 (ascl:2010.001).

molly analyzes 1D astronomical spectra. Its prime purpose is for handling large numbers of similar spectra (e.g., time series spectroscopy), but it contains many of the standard operations used for normal spectrum analysis as well. It overlaps with the various similar programs such as dipso (ascl:1405.016) and has strengths (particularly for time series spectra) and weaknesses compared to them.

MOLIERE-5 (Microwave Observation LIne Estimation and REtrieval) is a versatile forward and inversion model for the millimeter and submillimeter wavelengths range and includes an inversion model. The MOLIERE-5 forward model includes modules for the calculation of absorption coefficients, radiative transfer, and instrumental characteristics. The radiative transfer model is supplemented by a sensitivity module for estimating the contribution to the spectrum of each catalog line at its center frequency enabling the model to effectively filter for small spectral lines. The instrument model consists of several independent modules, including the calculation of the convolution of spectra and weighting functions with the spectrometer response functions. The instrument module also provides several options for modeling of frequency-switched observations. The MOLIERE-5 inversion model calculates linear Optimal Estimation, a least-squares retrieval method which uses statistical apriori knowledge on the retrieved parameters for the regularization of ill-posed inversion problems and computes diagnostics such as the measurement and smoothing error covariance matrices along with contribution and averaging kernel functions.

Molecfit corrects astronomical observations for atmospheric absorption features based on fitting synthetic transmission spectra to the astronomical data, which saves a significant amount of valuable telescope time and increases the instrumental efficiency. Molecfit can also estimate molecular abundances, especially the water vapor content of the Earth’s atmosphere. The tool can be run from a command-line or more conveniently through a GUI.

MOKA simulates the gravitational lensing signal from cluster-sized haloes. This algorithm implements recent results from numerical simulations to create realistic lenses with properties independent of numerical resolution and can be used for studies of the strong lensing cross section in dependence of halo structure.

ModeCode is a publicly available code that computes the primordial scalar and tensor power spectra for single field inflationary models. ModeCode solves the inflationary mode equations numerically, avoiding the slow roll approximation. It provides an efficient and robust numerical evaluation of the inflationary perturbation spectrum, and allows the free parameters in the inflationary potential to be estimated within an MCMC computation. ModeCode also allows the estimation of reheating uncertainties once a potential has been specified. It is interfaced with CAMB and CosmoMC to compute cosmic microwave background angular power spectra and perform likelihood analysis and parameter estimation. It can be run as a standalone code as well. Errors in the results from ModeCode contribute negligibly to the error budget for analyses of data from Planck or other next generation experiments.

ModeChord computes the primordial scalar and tensor power spectra for single field inflationary models. The code solves the inflationary mode equations numerically, avoiding the slow roll approximation. It provides an efficient and robust numerical evaluation of the inflationary perturbation spectrum, and allows the free parameters in the inflationary potential to be estimated. ModeChord also allows the estimation of reheating uncertainties once a potential has been specified.

mockFRBhosts estimates the fraction of FRB hosts that can be cataloged with redshifts by existing and future optical surveys. The package uses frbpoppy (ascl:1911.009) to generate a population of FRBs for a given radio telescope. For each FRB, a host galaxy is drawn from a data base generated by GALFORM (ascl:1510.005). The galaxies' magnitudes in different photometric surveys are calculated as are the number of bands in which they are detected. mockFRBhosts also calculates the follow-up time in a 10-m optical telescope required to do photometry or spectroscopy and provides a simple interface to Bayesian inference methods via MCMC simulations provided in the FRB package (ascl:2306.018).

MOCASSIN is a fully 3D or 2D photoionisation and dust radiative transfer code which employs a Monte Carlo approach to the transfer of radiation through media of arbitrary geometry and density distribution. Written in Fortran, it was originally developed for the modelling of photoionised regions like HII regions and planetary nebulae and has since expanded and been applied to a variety of astrophysical problems, including modelling clumpy dusty supernova envelopes, star forming galaxies, protoplanetary disks and inner shell fluorence emission in the photospheres of stars and disk atmospheres. The code can deal with arbitrary Cartesian grids of variable resolution, it has successfully been used to model complex density fields from SPH calculations and can deal with ionising radiation extending from Lyman edge to the X-ray. The dust and gas microphysics is fully coupled both in the radiation transfer and in the thermal balance.

MOBSE investigates the demography of merging BHBs. A customized version of the binary stellar evolution code BSE (ascl:1303.014), MOBSE includes metallicity-dependent prescriptions for mass-loss of massive hot stars and upgrades for the evolution of single and binary massive stars.

Mo’Astro is a MongoDB framework for observational astronomy pipelines. Mo'Astro sets up a MongoDB collection of a survey's image set, keeping FITS metadata readily available, and providing a place in the reduction pipeline to persist metadata. Mo’Astro also provides facilities for batch processing images with the Astromatic tool suite, and for hosting a local 2MASS star catalog with spatial-search built-in.

mnms (Map-based Noise ModelS) creates map-based models of Simons Observatory Atacama Cosmology Telescope (ACT) data. Each model supports drawing map-based simulations from data splits with independent realizations of the noise or equivalent, similar to an independent set of time-domain sims. In addition to the ability to create on-the-fly simulations, mnms also includes ready-made scripts for writing a large batch of products to disk in a dedicated SLURM job.

The MMIRS data reduction pipeline provides complete and flexible data reduction for long-slit and multi-slit spectroscopic observations collected using the MMT and Magellan Infrared Spectrograph (MMIRS). Written in IDL, it offers sky subtraction, correction for telluric absorpition, and is fast enough to permit real-time data reduction for quality control.

Make Me A Star (MMAS) quickly generates stellar collision remnants and can be used in combination with realistic dynamical simulations of star clusters that include stellar collisions. The code approximates the merger process (including shock heating, hydrodynamic mixing, mass ejection, and angular momentum transfer) with simple algorithms based on conservation laws and a basic qualitative understanding of the hydrodynamics. These simple models agree very well with those from SPH (smoothed particle hydrodynamics) calculations of stellar collisions, and the subsequent stellar evolution of these models also matches closely that of the more accurate hydrodynamic models.

MM-LSD (Multi-Mask Least-Squares Deconvolution) performs continuum normalization of 2D spectra (echelle order spectra). It also masks and partially corrects telluric lines and extracts RVs from spectra. The code requires RASSINE (ascl:2102.022) and uses spectral line data from VALD3.

The parallel Python framework MLZ (Machine Learning and photo-Z) computes fast and robust photometric redshift PDFs using Machine Learning algorithms. It uses a supervised technique with prediction trees and random forest through TPZ that can be used for a regression or a classification problem, or a unsupervised methods with self organizing maps and random atlas called SOMz. These machine learning implementations can be efficiently combined into a more powerful one resulting in robust and accurate probability distributions for photometric redshifts.

MLG simulates Gaia measurements for predicted astrometric microlensing events. It fits the motion of the lens and source simultaneously and reconstructs the 11 parameters of the lensing event. For lenses passing by multiple background sources, it also fits the motion of all background sources and the lens simultaneously. A Monte-Carlo simulation is used to determine the achievable precision of the mass determination.

MLC_EPGs classifies intermediate redshift (z = 0.3–0.8) emission line galaxies as star-forming galaxies, composite galaxies, active galactic nuclei (AGN), or low-ionization nuclear emission regions (LINERs). It uses four supervised machine learning classification algorithms: k-nearest neighbors (KNN), support vector classifier (SVC), random forest (RF), and a multi-layer perceptron (MLP) neural network. For input features, it uses properties that can be measured from optical galaxy spectra out to z < 0.8—[O III]/Hβ, [O II]/Hβ, [O III] line width, and stellar velocity dispersion—and four colors (u−g, g−r, r−i, and i−z) corrected to z = 0.1.

MLAPM simulates structure formation from collisionless matter. The code, written in C, is purely grid-based and uses a recursively refined Cartesian grid to solve Poisson's equation for the potential, rather than obtaining the potential from a Green's function. Refinements can have arbitrary shapes and in practice closely follow the complex morphology of the density field that evolves. The timestep shortens by a factor two with each successive refinement. It is argued that an appropriate choice of softening length is of great importance and that the softening should be at all points an appropriate multiple of the local inter-particle separation. Unlike tree and P3M codes, multigrid codes automatically satisfy this requirement.

mkj_libs provides a set of helper routines (vector operations, astrometry, statistical analysis of spherical data) for the main plane-fitting and analysis tools.

mixT accurately predicts T derived from a single-temperature fit for a multi-component thermal plasma. It can be applied in the deprojection analysis of objects with the temperature and metallicity gradients, for correction of the PSF effects, for consistent comparison of numerical simulations of galaxy clusters and groups with the X-ray observations, and for estimating how emission from undetected components can bias the global X-ray spectral analysis.

Mixclask combines Cloudy (ascl:9910.001) and SKIRT (ascl:1109.003) to predict spectra and gas properties in astrophysical contexts, such as galaxies and HII regions. The main output is the mean intensity of a region filled with stars, gas and dust at different positions, assuming axial symmetry. The inputs for Mixclask are the stellar and ISM data for each region and an file for the positions (x,y,z) that will be output.

MISTTBORN can simultaneously fit multiple types of data within an MCMC framework. It handles photometric transit/eclipse, radial velocity, Doppler tomographic, or individual line profile data, for an arbitrary number of datasets in an arbitrary number of photometric bands for an arbitrary number of planets and allows the use of Gaussian process regression to handle correlated noise in photometric or Doppler tomographic data. The code can include dilution due to a nearby unresolved star in the transit fits, and an additional line component due to another star or scattered sun/moonlight in Doppler tomographic or line profile fits. It can also be used for eclipsing binary fits, including a secondary eclipse and radial velocities for both stars. MISTTBORN produces diagnostic plots showing the data and best-fit models and the associated code MISTTBORNPLOTTER produces publication-quality plots and tables.

MiSTree quickly constructs minimum spanning tree graphs for various coordinate systems, including Celestial coordinates, by using a k-nearest neighbor graph (k NN, rather than a matrix of pairwise distances) which is then fed to Kruskal's algorithm to create the graph. MiSTree bins the MST statistics into histograms and plots the distributions; enabling the inclusion of high-order statistics information from the cosmic web to provide additional information that improves cosmological parameter constraints. Though MiSTree was designed for use in cosmology, it can be used in any field requiring extracting non-Gaussian information from point distributions.

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:

- add, edit, and remove FITS header keywords;
- split and join Multi-Extension-FITS (MEF) files;
- unpile and pile FITS data-cubes; and,
- create, check, and update FITS checksums, using R. Seaman’s protocol.

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.

MIRaGe creates simulated exposures for NIRCam’s imaging and wide field slitless spectroscopy (WFSS) modes, NIRISS’s imaging, WFSS, and aperture masking interferometery (AMI) modes, and FGS’s imaging mode. It supports sidereal as well as non-sidereal tracking; for example, sources can be made to move across the field of view within an observation.

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.

The MINDS hybrid pipeline is based on the JWST pipeline and routines from the VIP package (ascl:1603.003) for the reduction of JWST MIRI-MRS data. The pipeline compensates for some of the known weaknesses of the official JWST pipeline to improve the quality of spectrum extracted from MIRI-MRS data. This is done by leveraging the capabilities of VIP, another large data reduction package used in the field of high-contrast imaging.

The front end of the pipeline is a highly automated Jupyter notebook. Parameters are typically set in one cell at the beginning of the notebook, and the rest of the notebook can be run without any further modification. The Jupyter notebook format provides flexibility, enhanced visibility of intermediate and final results, more straightforward troubleshooting, and the possibility to easily incorporate additional codes by the user to further analyze or exploit their results.

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, Omega_SCO < 0.1 is favored for 10⁵ < M_SCO/M_odot < 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.

The 2-D wavelet transformation code MGwave detects kinematic moving groups in astronomical data; it can also investigate underdensities which can eventually provide further information about the MW's non-axisymmetric features. The code creates a histogram of the input data, then performs the wavelet transformation at the specified scales, returning the wavelet coefficients across the entire histogram in addition to information about the detected extrema. MGwave can also run Monte Carlo simulations to propagate uncertainties. It runs the wavelet transformation on simulated data (pulled from Gaussian distributions) many times and tracks the percentage of the simulations in which a given extrema is detected. This quantifies whether a detected overdensity or underdensity is robust to variations of the data within the provided errors.

MGPT (Modified Gravity Perturbation Theory) computes 2-point statistics for LCDM model, DGP and Hu-Sawicky f(R) gravity. Written in C, the code can be easily modified to include other models. Specifically, it computes the SPT matter power spectrum, SPT Lagrangian-biased tracers power spectrum, and the CLPT matter correlation function. MGPT also computes the CLPT Lagrangian-biased tracers correlation function and a set of Q and R functionsfrom which other statistics, as leading order bispectrum, can be constructed.

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).