Torch simulates coupled gas and N-body dynamics in astrophysical systems such as newly forming star clusters. It combines the FLASH (ascl:1010.082) code for gas dynamics and the ph4 code for direct N-body evolution via the AMUSE framework.
AstroHOG compares extended spectral-line observations (PPV cubes); the histogram of oriented gradients (HOG) technique takes as input two PPV cubes and provides an estimate of their spatial correlation across velocity channels to study spatial correlation between different tracers of the ISM.
PYSOLATOR removes the orbital modulation from a binary pulsar and/or its companion. In essence, it subtracts the predicted Roemer delay for the given orbit and then resamples the time series so as to make the signal appear as if it were emitted from the barycenter of the binary system, making the search for pulses easier and faster.
HOMER (Helper Of My Eternal Retrievals) is an MCMC-based inverse modeling code. Given some data and uncertainties, the code determines the posterior distribution of a model. HOMER uses MC3 (ascl:1610.013) for its MCMC; its forward model is a neural network (NN) surrogate model trained by MARGE (ascl:2003.010). The code produces plots of the 1D marginalized posteriors, 2D pairwise posteriors, and parameter history traces, and can also overplot the 1D and 2D posteriors for multiple MCMC runs for comparison with other retrieval results. HOMER also computes the Bhattacharyya coefficient to compare the similarity of 1D marginalized posteriors.
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 with a random uniform sampler to propose models within a defined parameter space. Due to the possible data size, MARGE also modifies MC3 (ascl:1610.013) to save out the evaluated models in batches.
TOASTER is a pulse times-of-arrival (TOA) tracker. It stores reduced/folded observations, meta data, templates, parfiles, TOAs, and timefiles in an organized manner using an SQL database. TOASTER also provides a full-featured python toolkit for reliably interacting with the data and database, and provides scripts that, for example, list and summarize the TOAs in the data base, and generate TOA files in multiple formats. The framework can also be used to generate TOAs from observations using flexible and reproducible plugins referred to as "manipulators".
CoastGuard reduces Effelsberg data; it is written in python and based on PSRCHIVE (ascl:1105.014). Though primarily designed for Effelsberg PSRIX data, it contains components sufficiently general for use with psrchive-compatible data files from other observing systems. In particular, the radio frequency interference (RFI) removal algorithm has been applied to data from the Parkes Telescope and has also been adopted by the LOFAR pulsar timing data reduction pipeline.
RAPID (Real-time Automated Photometric IDentification) classifies multiband photometric light curves into several different transient classes. It uses a deep recurrent neural network to produce time-varying classifications, and because it does not rely on deriving computationally expensive features from the data, it is well suited for processing alerts that wide-field surveys such as the Zwicky Transient Facility (ZTF) and the Large Synoptic Survey Telescope (LSST) will produce.
PORTAL (POlarized Radiative Transfer Adapted to Lines), a 3D polarized radiative transfer code, simulates the emergence of polarization in the emission of atomic or molecular (sub-)millimeter lines. Written in Fortran90, PORTAL can be used in standalone mode or can process the output of other 3D radiative transfer codes
The RHT (Rolling Hough Transform) measures linear intensity as a function of orientation in images. The machine vision algorithm quantifies the alignment of H I fibers with the magnetic field, and allows mapping of detailed structure in a magnetic field, suggesting that this technique can be used for for resolved field strength estimation.
scousepy is a Python implementation of spectral line-fitting IDL code SCOUSE (ascl:1601.003). It fits a large amount of complex astronomical spectral line data in a systematic way.
acorns generates a hierarchical system of clusters within discrete data by using an n-dimensional unsupervised machine-learning algorithm that clusters spectroscopic position-position-velocity data. The algorithm is based on a technique known as hierarchical agglomerative clustering. Although acorns was designed with the analysis of discrete spectroscopic position-position-velocity (PPV) data in mind (rather than uniformly spaced data cubes), clustering can be performed in n-dimensions and the algorithm can be readily applied to other data sets in addition to PPV measurements.
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).
TESS-Point converts astronomical target coordinates given in right ascension and declination to detector pixel coordinates for the MIT-led NASA Transiting Exoplanet Survey Satellite (TESS) spacecraft. The program can also provide detector pixel coordinates for a star by TESS input catalog identifier number and common astronomical name. Tess-point outputs the observing sector number, camera number, detector number, and pixel column and row.
PulseBlast 2 is a mass directory scanner for Unix, Darwin and DOS/NT kernels that allows the user to analyze PSRFITS data all the way from raw time series to fully calibrated and RFI excised TOAs. Many PSRFITS software packages in the past have dealt with data on the scale of a single file and many optimization methods for pulsar data currently used are used in PulseBlast 2 for individual file analysis. However, what sets PulseBlast 2 apart is its optimization across many directories spread out across the OS. The program is entirely written in Python 3, with a scope to change string operations into Perl scripts.
We present the software system developed to implement the Locus Algorithm, a novel algorithm designed to maximise the performance of differential photometry systems by optimising the number and quality of reference stars in the Field of View with the target.
DISKMODs provides radial structure models of accretion disk solutions. The following models are included: Novikov-Thorne thin disk model and Sadowski polytropic slim disk model. Each model implements a common interface that gives the radial dependence of selected geometrical, physical and thermodynamic quantities of the accretion flow. The model interpolates through a set of tabulated numerical solutions. These solutions are computed for a reference mass M=10 Msun. The model can rescale the disk structure to any mass, with masses in the range of 5-20 Msun giving reasonably good results.
CR-SISTEM models lunar orbital and rotational dynamics, taking into account the effects of a liquid core. Orbits of the Moon and Earth are fully integrated, and other planets (or additional point-mass satellites) may be included in the integration. Lunar and solar tides on Earth, eccentricity and obliquity tides on the Moon, and lunar core-mantle friction are included. The integrator is one file (crsistem5.for) written in FORTRAN 90, uses seven input files (settings.in, planets.in, moons.in, tidal.in, lunar.in, precess.in and core.in), and has at least eight output files (planet101.out, moon101.out, pole.out, spin_orb.out, spin_ecl.out, cspin_ecl.out, long.out and clong.out); additional moons and planets would add more output. The input files provided with the code set up a 1 Myr simulation of a slow-spinning Moon on an orbit of 40 Earth radii, which will then dynamically relax to the lowest-energy state (in this case it is a synchronous rotation with a core spinning separately from the mantle).
ExoCAM adapts the NCAR Community Earth System Model (CESM) for planetary and exoplanetary applications. The system files, source code, initial conditions files, and namelists provided do not run standalone. ExoCAM is a patch to be used with standard distributions of CESM version 1.2.1 (http://www.cesm.ucar.edu/models/current.html), and is also intended to be run with ExoRT (ascl:2002.019), a correlated-k radiative transfer package.
ExoRT is a flexible, two-stream radiative transfer code that interfaces with CAM/CESM (http://www.cesm.ucar.edu/models/current.html) or 1D offline; it is also used with ExoCAM (ascl:2002.020). Quadrature is used for shortwave and hemispheric mean is used for longwave. The gas phase optical depths are calculate using a correlated K-distribution method, with overlapping bands treated using an amount weighted scheme. Cloud optics are treated using mie scattering for both liquid and ice clouds, and cloud overlap is treated using Monte Carlo Independent Column Approximation.
Bayesfit pulls together Tempo2 (ascl:1210.015) and MultiNest (ascl:1109.006) components to provide additional functionality such as the specification of priors; Nelder–Mead optimization of the maximum-posterior point; and the capability of computing the partially marginalized likelihood for a given subset of timing-model parameters. Bayesfit is a single python command-line application.
libstempo uses the Tempo2 library (ascl:1210.015) to load a pulsar's tim/par files, providing Python access to the TOAs, the residuals, the timing-model parameters, the fit procedure, and more.
Cobra uses single pulse time series data to search for and time pulsars, performing a fully phase coherent timing analysis. The GPU-accelerated Bayesian analysis package, written in Python, incorporates models for both isolated and accelerated systems, as well as both Keplerian and relativistic binaries. Cobra builds a model pulse train that incorporates effects such as aliasing, scattering and binary motion and a simple Gaussian profile and compares this directly to the data; the software can thus combine data over multiple frequencies, epochs, or even across telescopes.
GizmoAnalysis reads and analyzes N-body simulations run with the Gizmo code (ascl:1410.003). Written in Python, it was developed primarily to analyze FIRE simulations, though it is usable with any Gizmo snapshot files. It offers the following functionality: reads snapshot files and converts particle data to physical units; provides a flexible dictionary class to store particle data and compute derived quantities on the fly; plots images and properties of particles; and generates region files for input to MUSIC (ascl:1311.011) to generate cosmological zoom-in initial conditions. GizmoAnalysis also computes rates of supernovae and stellar winds, including their nucleosynthetic yields, as used in FIRE simulations. The software package includes a tutorial in a Jupyter notebook.
HaloAnalysis reads and analyzes halo/galaxy catalogs, generated from Rockstar (ascl:1210.008) or AHF (ascl:1102.009), and merger trees generated from Consistent Trees (ascl:1210.011). Written in Python, it offers the following functionalities: reads halo/galaxy/tree catalogs from multiple file formats; assigns baryonic particles and galaxy properties to dark-matter halos; combines and re-generates halo/galaxy/tree files in hdf5 format; analyzes properties of halos/galaxies; and selects halos to generate zoom-in initial conditions. The code includes a tutorial in the form of a Jupyter notebook.
GWecc computes the pulsar timing array (PTA) signals induced by eccentric supermassive binaries. Written in C++, it computes the plus/cross polarizations as well as Earth and pulsar terms of the PTA signal given the binary parameters and the sky locations of the binary and the pulsar. A python wrapper is included through which GWecc can be used to simulate, search for and constrain gravitational wave-emitting eccentric supermassive binaries using packages such as ENTERPRISE (ascl:1912.015) and libstempo (ascl:2002.017).
DMRadon calculates the Radon Transform for use in the analysis of Directional Dark Matter Direct Detection. The code can calculate speed distributions, velocity distribution, velocity integral (eta) and Radon Transforms or a standard Maxwell-Boltzmann distribution. DMRadon also calculates the velocity distribution averaged over different angular bins.
PyHammer performs rapid and automatic spectral classification of stars according to the Morgan-Keenan classification system; it is a Python revision of the IDL code The Hammer (ascl:1405.003) and offers additional capabilities. Working in the range of 3,650-10,200 Angstroms, the automatic spectral typing algorithm compares important spectral lines to template spectra and determines the best matching spectral type, ranging from O to L type stars. The code can also determine a star's metallicity ([Fe/H]) and radial velocity shifts. Once the automatic classification algorithm has run, PyHammer provides the user an interface for determining spectral types visually by comparing their spectra to provided templates.
Apercal is a dedicated, automated data reduction and analysis pipeline written for the Apertif (APERture Tile In Focus) upgrade to the Westerbork Synthesis Radio Telescope. This upgrade dramatically increases the field of view and survey speed of the telescope and is being used for survey observations that can produce 5 terabytes of data for each observation. Apercal uses existing and new tools and parallelization to provide the performance needed for the large volume of data produced Apertif surveys. The software is written entirely in Python and uses third–party astronomical software, such as AOFlagger (ascl:1010.017), CASA (ascl:1107.013), and Miriad (ascl:1106.007), for certain tasks. Apercal is modular, making it possible to run specific modules manually instead of the full pipeline, and information can be exchanged between modules because status parameters are written and read from a python pickled dictionary file. The pipeline can also run fully automatically.
DASH classifies the type, age, redshift and host for any supernova spectra based on the learned features, through use of a deep convolutional neural network to train a matching algorithm, of each supernova’s type and age. The Python library allows a user to classify spectra; the software is fast and can classify thousands of spectra in seconds. A graphical interface that enables a user to view and classify a spectrum is also available.
ExoSim models host star and planet transit events, simulating the temporal change in stellar flux due to the light curve. It is wavelength-dependent, using an input planet spectrum to determine the light curve depth for any given wavelength and can capture temporal effects, such as correlated noise. ExoSim's star spot simulator produces simulated observations that include spot and facula contamination. The code is flexible and can be generically applied to different instruments that simulate specific time-dependent processes.
ProSpect generates good quality SEDs that can be used to estimate the broad band photometric properties of galaxies that have known star formation and gas metallicity histories. It allows for complex star formation and metallicity histories to be specified, and can be used in a generative or fitting (Bayesian) mode. ProSpect provides a high level interface to the BC03 (low and high resolution) and EMILES libraries, as well as the Dale 2014 dust emission templates. Its source code is available for download, and it is also available as an interactive web tool.
ScamPy "paints" an observed population of cosmological objects on top of the DM-halo/subhalo hierarchy obtained from DM-only simulations. The method combines the Halo Occupation Distribution (HOD) method with sub-halo abundance matching (SHAM); the two processes together are dubbed Sub-halo clustering and abundance matching (SCAM). The procedure requires applying the two methods in sequence; by applying the HOD scheme, the host sub-haloes are selected, and the SHAM algorithm associates an observable property of choice to each sub-halo. The provided python interface allows users to load and populate DM halos and sub-halos obtained by FoF and SUBFIND algorithms applied to DM snapshots at any redshift. The software is highly-optimized and flexible.
ODUSSEAS (Observing Dwarfs Using Stellar Spectroscopic Energy-Absorption Shapes) uses machine learning to derive the Teff and [Fe/H] of M dwarf stars by using their optical spectra obtained by different spectrographs with different resolutions. The software uses the measurement of the pseudo equivalent widths for more than 4000 stellar absorption lines and the machine learning Python package scikit-learn (https://scikit-learn.org/stable/) to predict the stellar parameters.
triceratops (Tool for Rating Interesting Candidate Exoplanets and Reliability Analysis of Transits Originating from Proximate Stars) validates planet candidates from the Transiting Exoplanet Survey Satellite (TESS). The code calculates the probabilities of a wide range of transit-producing scenarios using the primary transit of the planet candidate and preexisting knowledge of its host and nearby stars. It then uses the known properties of these stars to calculate star-specific priors for each scenario with estimates of stellar multiplicity and planet occurrence rates.
ORIGIN performs blind detection of faint emitters in MUSE datacubes. The algorithm is tuned to detect faint spatial-spectral emission signatures while allowing for a stable false detection rate over the data cube, and providing in the same time an automated and reliable estimation of the purity. ORIGIN implements a nuisance removal part based on a continuum subtraction combining a Discrete Cosine Transform and an iterative Principal Component Analysis and a detection part based on the local maxima of Generalized Likelihood Ratio test statistics obtained for a set of spatial-spectral profiles of emission line emitters. In addition, it performs a purity estimation in which the proportion of true emission lines is estimated from the data itself: the distribution of the local maxima in the noise only configuration is estimated from that of the local minima.
The massively parallel code RASCAS (RAdiative SCattering in Astrophysical Simulations) performs radiative transfer on an adaptive mesh with an octree structure using the Monte Carlo technique. The code features full MPI parallelization, domain decomposition, adaptive load-balancing, and a standard peeling algorithm to construct mock observations. The radiative transport of resonant line photons through different mixes of species (e.g. HI, SiII, MgII, FeII), including their interaction with dust, is implemented in a modular fashion to allow new transitions to be easily added to the code. RASCAS may also be used to propagate photons at any wavelength (e.g. stellar continuum or fluorescent lines), and has been designed to be easily customizable and to process simulations of arbitrarily large sizes on large supercomputers.
SDAR (Slow-Down Algorithmic Regularization) simulates the long-term evolution of few-body systems such as binaries and triples. The algorithm used provides a few orders of magnitude faster performance than the classical N-body method. The secular evolution of hierarchical systems, e.g. Kozai-Lidov oscillation, can be well reproduced. The code is written in the C++ language and can be used either as a stand-alone tool or a library to be plugged in other N-body codes. The high precision of the floating point to 62 digits is also supported.
The protocol describes the algorithm of arriving at LOD in a given past geological Epoch. First the lunar orbital radius of the given geologic epoch has to be determined. For this the velocity of recession of Moon for the accelerated phase has to be determined. The spatial integral of the reciprocal of Velocity of recession gives the the transit time of Moon from desired orbit to the present orbit.Through several iterations the transit time is made to converge on the geologic epoch. Once we determine the desired orbital radius it has to be substituted in the LOD expression to determine the LOD in the given geologic epoch.
MERA works with large 3D AMR/uniform-grid and N-body particle data sets from astrophysical simulations such as those produced by the hydrodynamic code RAMSES (ascl:1011.007) and is written entirely in the Julia language. The package provides essential functions for efficient and memory lightweight data loading and analysis. The core of MERA is a database framework.
The project is a simple Python client for Cosmicflows-3 Distance-Velocity Calculator at distances less than 400 Mpc (http://edd.ifa.hawaii.edu/CF3calculator/)
Compute expectation distances or velocities based on smoothed velocity field from the Wiener filter model of https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.5438G/abstract.
StarburstPy is a python wrapper for Starburst99 (ascl:1104.003). The code contains methods for setting all inputs, running Starburst99, and reading output data into python dictionaries.
gnm is an implementation of the affine-invariant sampler for Markov chain Monte Carlo (MCMC) that uses the Gauss-Newton-Metropolis (GNM) Algorithm. The GNM algorithm is specialized in sampling highly non-linear posterior probability distribution functions of the form exp(-||f(x)||^2/2). The code includes dynamic hyper-parameter optimization to increase performance of the sampling; other features include the Jacobian tester and an error bars creator.
The NVIDIA GPU-based pipeline code peasoup provides a one-step pulsar search, including searching for pulsars with up to moderate accelerations, with only one command. Its features include dedispersion, dereddening in the Fourier domain, resampling, peak detection, and optional time series folding. peasoup's output is the candidate list.
RPPPS (Re-analysing Pipeline for Parkes Pulsar Survey) uses Linux shell scripts, C language, and python code and two parallel strategies to reorganize the PRESTO (ascl:1107.017) pulsar search pipeline to run multiple processes in parallel, thus accelerating the search for pulsars. Though originally designed for reprocessing PMPS data, the code has also been successfully used with FAST (Five-hundred-meter Aperture Spherical radio Telescope) drift scan data. The pipeline is only CPU-based and can be easily and quickly deployed in computing nodes for testing purposes or data processes.
MCMCI (Markov chain Monte Carlo + isochrones) characterizes a whole exoplanetary system directly by modeling the star and its planets simultaneously. The code, written in Fortran, uses light curves and basic stellar parameters with a transit analysis algorithm that interacts with stellar evolutionary models, thus using both model-dependent and empirical age indicators to characterize the system.
ExoTETHyS models exoplanetary transits, eclipsing binaries, and related phenomena. The package calculates stellar limb-darkening coefficients down to <10 parts per million (ppm) and generates an exact transit light-curve based on the entire stellar intensity profile rather than limb-darkening coefficients.
CosMOPED (Cosmological MOPED) uses the MOPED (Multiple/Massively Optimised Parameter Estimation and Data compression) compression scheme to compress the Planck power spectrum. This convenient and lightweight compressed likelihood code is implemented in Python. To compute the likelihood for the LambdaCDM model using CosMOPED, one needs only six compression vectors, one for each parameter, and six numbers from compressing the Planck data using the six compression vectors. Using these, the likelihood of a theory power spectrum given the Planck data is the product of six one-dimensional Gaussians. Extended cosmological models require computing extra compression vectors.
ORCS (Outils de Réduction de Cubes Spectraux) is an analysis engine for SITELLE spectral cubes. The software extracts integrated spectra, fits the sinc emission lines, and recalibrates data in wavelength, astrometry and flux. ORCS offers a choice between a Bayesian or a classical fitting algorithm , and also provides automatic source detection and radial velocity correction.
DebrisDiskFM provides forward modeling for circumstellar debris disks in scattered light using the MCFOST disk modeling software to generate disk model images using given input parameters and emcee (ascl:1303.002) to obtain the posterior distributions for these parameters.
Behind The Spectrum (BTS) is a fully-automated multiple-component fitter for optically-thin spectra. Written as a python module, the routine uses the first, second and third derivatives to determine thenumber of components in the spectrum. A least-squared fitting routine then determines the best fit with that number of components, checking for over-fitting and over-lapping velocity centroids.
Protostellar Evolution simulates the evolution of stellar stellar radius and luminosity from the bound core stage through to the core hydrogen ignition as a zero-age main-sequence (ZAMS) star and beyond. Written in Fortran 90, the code is implemented as a module of the FLASH astrophysical fluid dynamics code (ascl:1010.082).
The CASA (1107.013) task FAKEOBS generates model visibilities from already-existing measurement sets. This task can be used to substitute all the visibilities of the target with simulations computed from any model image. The measurement can either be with real or simulated data, the target can have been observed in mosaic mode, and there can be several sources (e.g., bandpass calibrator, flux/phase calibrator, and target).
FragMent studies fragmentation in filaments by collating a number of different techniques, including nearest neighbour separations, minimum spanning tree, two-point correlation function, and Fourier power spectrum. It also performs model selection using a frequentist and Bayesian approach to find the best descriptor of a filament's fragmentation. While the code was designed to investigate filament fragmentation, the functions are general and may be used for any set of 2D points to study more general cases of fragmentation.
sf3dmodels models star-forming regions; it brings together analytical models in order to compute their physical properties in a 3-dimensional grid. The package can couple different models in a single grid to recreate complex star forming systems such as those being revealed by current instruments. The output data can be read with LIME (ascl:1107.012) or RADMC-3D (ascl:1108.016) to carry out radiative transfer calculations of the modeled region.
TRANSPHERE is a simple dust continuum radiative transfer code for spherically symmetric circumstellar envelopes. It handles absorption and re-emission and computes the dust temperature self-consistently; it does not, however, deal with scattering. TRANSPHERE uses a variable eddington factor method for the radiative transfer. The RADMD code (ascl:1108.016) is more versatile, but for a spherically symmetric problem for which scattering is of much concern, it may be easier to use a simple code such as TRANSPHERE.
Please note that this code has not been updated since 2006.
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.
amber_meta integrates a few routines to launch AMBER (the “Apertif Monitor for Bursts Encountered in Real-time”, https://github.com/AA-ALERT/AMBER) in a systematic manner. To avoid typing a string in the command line manually with all parameters required to launch AMBER, amber_meta generates the command from configuration files, and can directly launch AMBER instances.
Time-domain astronomy sandbox consists in a series of classes to simulate and process time-domain astronomy data products in Python. The code was originally developed to model Fast Radio Burst (FRB) and Radio Frequency Interference (RFI), and evaluate different RFI mitigation methods and their effect on FRB search.
JetSeT is an open source C/Python framework to reproduce radiative and accelerative processes acting in relativistic jets, allowing to fit the numerical models to observed data. The main features of this framework are:
- Handling observed data: re-binning, definition of data sets, bindings to astropy tables and quantities
definition of complex numerical radiative scenarios: Synchrotron Self-Compton (SSC), external Compton
(EC) and EC against the CMB
- Constraining of the model in the pre-fitting stage, based on accurate and already published
phenomenological trends. In particular, starting from phenomenological parameters, such as spectral indices,
peak fluxes and frequencies, and spectral curvatures, that the code evaluates automatically, the pre-fitting
algorithm is able to provide a good starting model, following the phenomenological trends that I have
implemented. fitting of multiwavelength SEDs using both frequentist approach (iminuit) and bayesian MCMC
- Self-consistent temporal evolution of the plasma under the effect of radiative and accelerative processes,
both first order and second order (stochastic acceleration) processes.
Dual Active Nuclei Galaxies (DAGNs) are rare occurrences in the sky. Until now, most AGNs have been described to be found serendipitously, or by manual observation. In recent years, there has been an increasing interest in such dual AGNs and their astrophysical properties. Their study is important to the understanding of galaxy formation, star formation and these objects are the precursors to Gravitational Wave Sources.
Hence, we have devised a pipeline, that along with systematic data collection, can detect such dual AGN candidates. A novel algorithm 'Graph-Boosted Gradient Ascent' has been devised to detect whether an R-band image of a galaxy is a potential candidate for a DAGN or not. The pipeline can be cloned to a user's machine, and by joining the AstrIRG_DAGN group on SciServer, astronomers can collectively contribute to the mining of DAGNs.
Since early 2018, the Kepler/K2 project has been performing a uniform global reprocessing of data from K2 Campaigns 0 through 14. Subsequent K2 campaigns (C15-C19) are being processed using the same processing pipeline. One of the major benefits of the reprocessing effort is that, for the first time, short-cadence (1-min) light curves are produced in addition to the standard long-cadence (30-min) light curves. Users have been cautioned that the Kepler pipeline detrending module (PDC), developed for use on original Kepler data, has not been tailored for use on short-cadence K2 observations. Systematics due to events on fast timescales, such as thruster firings, are sometimes poorly corrected for many short-cadence targets. A Python data visualization and manipulation tool, called Kepler-K2 Cadence Events, has been developed that identifies and removes cadences associated with problematic thruster events, thus producing better light curves. Kepler-K2 Cadence Events can be used to visualize and manipulate light curve files and target pixel files from the Kepler, K2, and TESS missions. This software is available at the following NASA GitHub repository https://github.com/nasa/K2CE .
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.
STACKER stacks sources in interferometric data, i.e., averaging emission from different sources. The library allows stacking to be done directly on visibility data as well as in the image domain. The code is in format of a CASA (ascl:1107.013) task and implements uv- and image-stacking algorithms; it also provides several useful tasks for stacking related data processing. It allows introduction and stacking of random sources to estimate bias and noise, and also allows removal of a model of bright sources from the data.
Tangos builds databases (along the lines of Eagle or MultiDark) for cosmological and zoom simulations. Its
modular system generates and queries databases. It is designed to store and manage results from a user's own analysis code, provides web and python interfaces, and allows users to construct science-focused queries, including across entire merger trees, without requiring knowledge of SQL. Tangos manages the process of populating the database with science data, including auto-parallelizing the analysis. It can be customized to work with multiple python modules such as pynbody (ascl:1305.002) or yt (ascl:1011.022) to process raw simulation data; it defaults to using SQLite, but allows use of other databases as the underlying store through the use of SQLAlchemy.
PTMCMCSampler performs MCMC sampling using advanced techniques. The code implements a variety of proposal schemes, including adaptive Metropolis, differential evolution, and parallel tempering, which can be used together in the same run.
The Python package GWpy analyzes and characterizes gravitational wave data. It provides a user-friendly, intuitive interface to the common time-domain and frequency-domain data produced by the LIGO and Virgo observatories and their analyses. The core Python infrastructure is influenced by, and extends the functionality of, the Astropy (ascl:1304.002) package, and its methodology has been derived from, and augmented by, the LIGO Algorithm Library Suite (LALSuite), a large collection of primarily C99 routines for analysis and manipulation of data from gravitational-wave detectors. These packages use the SWIG program to produce Python wrappings for all C modules, allowing the GWpy package to leverage both the completeness, and the speed, of these libraries.
ENTERPRISE (Enhanced Numerical Toolbox Enabling a Robust PulsaR Inference SuitE) is a pulsar-timing analysis code which performs noise analysis, gravitational-wave searches, and timing model analysis. It uses Tempo2 (ascl:1210.015) to find the maximum-likelihood fit for the timing parameters and the basis of the fit for the red noise parameters if they are significant.
HARMPI is a parallel, 3D version of HARM (ascl:1209.005), which solves hyperbolic partial differential equations in conservative form using high-resolution shock-capturing techniques. The code is parallelized using MPI and is fully operational in 3D. HARMPI, like HARM, is capable of using non-uniform grids and solves the relativistic magnetohydrodynamic equations of motion on a stationary black hole spacetime in Kerr-Schild coordinates to evolve an accretion disk model.
GriSPy (Grid Search in Python) uses a regular grid search algorithm for quick fixed-radius nearest-neighbor lookup. It indexes a set of k-dimensional points in a regular grid providing a fast approach for nearest neighbors queries. Optional periodic boundary conditions can be provided for each axis individually. GriSPy implements three types of queries: bubble, shell and the nth-nearest, and offers three different metrics of interest in astronomy: the Euclidean and two distance functions in spherical coordinates of varying precision, haversine and Vincenty. It also provides a custom distance function. GriSPy is particularly useful for large datasets where a brute-force search is not practical.
GAME infers different ISM physical properties by analyzing the emission line intensities in a galaxy spectrum. The code is trained with a large library of synthetic spectra spanning many different ISM phases, including HII (ionized) regions, PDRs, and neutral regions. GAME is based on a Supervised Machine Learning algorithm called AdaBoost with Decision Trees as base learner. Given a set of input lines in a spectrum, the code performs a training on the library and then evaluates the line intensities to give a determination of the physical properties. The errors on the input emission line intensities and the uncertainties on the physical properties determinations are also taken into account. GAME infers gas density, column density, far-ultraviolet (FUV, 6–13.6 eV) flux, ionization parameter, metallicity, escape fraction, and visual extinction. A web interface for using the code is available.
QSOSIM realistically simulates high-resolution quasar spectra using a set of basic parameters (magnitude, redshift, and spectral index). The simulated spectra include physical effects seen in the real data: the power-law quasar continuum, the narrow and broad emission lines, absorption by neutral hydrogen (HI) in the Lyman alpha forest, and heavy element transitions along the line of sight. The code uses empirical HI column density, redshift, and b-parameter distributions to simulate absorption in the Lyman alpha forest. All absorbers with column densities larger than log [N(HI)/cm2]>17 have heavy element absorption, for which the column densities are calculated using the plasma simulation code CLOUDY (ascl:9910.001) and the radiative transfer code CUBA. The code also simulates the clustering of the intergalactic medium along the line of sight, the proximity effect of the quasar, and the effect of the cosmic ultraviolet background. Each simulated spectrum is saved in a single FITS file in as a noiseless R=100000 spectrum, as well as a spectrum convolved with Sloan Digital Sky Survey resolution (R=10000) and realistic noise.
AstroAccelerate processes time-domain radio astronomy data. It offers a standalone code that can be used to process filterbank data and a library that performs GPU-accelerated single pulse processing (SPS), Fourier Domain Acceleration Searching (FDAS) and dedispersion in real-time on very large data-sets comparable to those that will be produced by next-generation radio telescopes such as the SKA. AstroAccelerate uses NVIDIAR GPUs, and is configurable, stable, and easily maintained.
FORSTAND constructs dynamical models of galaxies using the Schwarzschild orbit-superposition method; the method is available as part of the AGAMA (ascl:1805.008) framework. The models created are constrained by line-of-sight kinematic observations and are applicable to galaxies of all morphological types, including disks and triaxial rotating bars.
The heterogeneity of papers dealing with the discovery and characterization of exoplanets makes every attempt to maintain a uniform exoplanet catalog almost impossible. Four sources currently available online (NASA Exoplanet Archive, Exoplanet Orbit Database, Exoplanet Encyclopaedia, and Open Exoplanet Catalogue) are commonly used by the community, but they can hardly be compared, due to discrepancies in notations and selection criteria.
Exo-MerCat is a Python code that collects and selects the most precise measurement for all interesting planetary and orbital parameters contained in the four databases, accounting for the presence of multiple aliases for the same target. It can download information about the host star as well by the use of Virtual Observatory ConeSearch connections to the major archives such as SIMBAD and those available in VizieR. A Graphical User Interface is provided to filter data based on the user's constraints and generate automatic plots that are commonly used in the exoplanetary community.
With Exo-MerCat, we retrieved a unique catalog that merges information from the four main databases, standardizing the output and handling notation differences issues. Exo-MerCat can correct as many issues that prevent a direct correspondence between multiple items in the four databases as possible, with the available data. The catalog is available as a VO resource for everyone to use and it is periodically updated, according to the update rates of the source catalogs.
PyFOSC is a pipeline toolbox for long-slit spectroscopy data reduction written in Python. It can be used for FOSC (Faint Object Spectrograph and Camera) data from Xinglong/Lijiang 2-meter telescopes in China. This pipeline privodes a neat way for data pre-processing, including updating missing header fileds for BFOSC data, reducing fits file extension for YFOSC data, etc. And it makes the data reduction procedure efficient by using previously identified lamp spectra as re-identification references during wavelength calibration, and applying multiprocessing in some modules. PyFOSC also enables customization for any other long-slit spectroscopy data.
PopSyCLE performs compact object population synthesis while taking photometric and astrometric microlensing effects into consideration. It uses Galaxia (ascl:1101.007) to produces a synthetic survey, injects compact objects into the resulting survey, and then produces a list of microlensing events, enabling the discovery of black holes with microlensing. It can be used to examine historical microlensing events from photometric surveys to statistically constrain the abundance of black holes in our galaxy, and to forward model microlensing survey results to constrain, for example, the properties of compact objects, Galactic structure, and the initial-final mass relation.
anesthetic brings together tools for processing nested sampling chains, leveraging standard scientific python libraries. The code provides computation of Bayesian evidences, Kullback-Liebler divergences and Bayesian model dimensionalities, marginalized 1d and 2d plots, and dynamic replaying of nested sampling. anesthetic was designed primarily for use with nested sampling outputs, although it can be used for normal MCMC chains.
HSIM simulates observations with HARMONI on the Extremely Large Telescope. HSIM takes high spectral and spatial resolution input data cubes, encoding physical descriptions of astrophysical sources, and generates mock observed data cubes. The simulations incorporate detailed models of the sky, telescope, instrument, and detectors to produce realistic mock data. HSIM performs in-depth simulations for several key science cases as part of the design and development of the HARMONI integral field spectrograph, including the ELT AO performance, atmospheric effects and realistic detector statistics.
Athena++ is a complete re-write of the Athena astrophysical magnetohydrodynamics (MHD) code (ascl:1010.014) in C++. Compared to earlier versions, the Athena++ code has much more flexible coordinate and grid options and supports new physics. It also offers significantly improved performance and scalability, and improved source code clarity and modularity. Athena++ supports compressible hydrodynamics and MHD in 1D, 2D, and 3D, and special and general relativistic hydrodynamics and MHD. In addition, it supports Cartesian, cylindrical, or spherical polar coordinates; static or adaptive mesh refinement in any coordinate system; mixed parallelization with both OpenMP and MPI; and a task-based execution model for improved load balancing, scalability and modularity.
DALiuGE provides a distributed data management platform and a scalable pipeline execution environment to support continuous, soft real-time, data-intensive processing for producing radio astronomy data products; it originated from a prototyping activity as part of the SKA SDP Consortium called Data Flow Management System (DFMS). Though the development of DALiuGE is largely based on radio astronomy processing requirements, it has adopted a generic, data-driven framework architecture potentially applicable to many other data-intensive applications.
ASKAPsoft provides data processing functionality for Australian Square Kilometre Array Pathfinder, including calibration, spectral line imaging, continuum imaging, source detection and generation of source catalogs, and transient detection. The MPI-based package is the primary software for storing and processing raw data, and initiating the archiving of resulting science data products into the data archive (CASDA). The processing pipelines within ASKAPsoft are largely written in C++ built on top of casacore (ascl:1912.002) and other third party libraries.
The casacore package contains the core libraries of the old AIPS++/CASA (ascl:1107.013) package. This split was made to get a better separation of core libraries and applications. CASA is now built on top of Casacore. The system consists of a set of layered libraries (packages) and includes a library (using Boost-Python) that converts the basic Casacore types (e.g., Array, Record) to and from Python.
Casacore includes the casa package for core functionality and data types like Array and Record; a scimath package for N-dim functions with auto-differentiation and linear or non-linear fitting; and a
tables package for the table data system supporting N-dim arrays with advanced querying. It also includes the measures package to manage values in astronomical reference frames using physical units (Quanta) and the MeasurementSets for storing data in the UV-domain, and also the images package for N-dim images in world coordinates with various analysis operations.
Polyspectrum computes the polyspectrum from 3D grids using a fast Fourier transformation (FFT) estimator. The code, written in C and MPI-parallelized, support the computation of power- and bispectra; it also supports higher-order polyspectra, but streamlining the input data is required.
comb is a single-dish radio astronomy spectral line data reduction and analysis package developed at AT&T Bell labs and was used for data reduction for many single-dish telescopes, including Bell Labs 7-m, NRAO 12-m, DSN network, FCRAO 14-m, Arecibo, AST/RO, SEST, BIMA, and in 2011-2012, the Stratospheric Terahertz Observatory. A cookbook for the code is available.
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.
FFTLog-and-beyond takes the FFTLog algorithm for single-Bessel integrals and generalizes it for integrals containing a derivative of the Bessel function to solve the non-Limber integrals. The full non-Limber angular power spectrum integral is simplified by noting the small contribution from unequal-time nonlinear terms; this significantly reduces the computation and avoids the double-Bessel integral. The original FFTLog algorithm is also extended to compute integrals containing derivatives of Bessel functions, which can be used to efficiently compute angular power spectra including redshift-space distortions (RSD) and Doppler effects. C and Python versions of the code are available.
TreeFrog reads in particle IDs information between various structure catalogs and cross matches catalogs, assuming that particle IDs are unique and constant across snapshots. Though it is built as a cross correlator (in that it can match particles across several different catalogs), its principle use is as halo merger tree builder. TreeFrog produces links between objects found at different snapshots (or catalogs) and uses several possible functions to evaluate the merit of a link between one object at a given snapshot (or in a given catalog) to another object in a previous snapshot (or different catalog). It can also produce a full graph. The code utilizes MPI and OpenMP. It is optimzed for reading VELOCIraptor (ascl:1911.020) output but can also read output from other structure finders such as AHF (ascl:1102.009).
VELOCIraptor-STF, formerly STructure Finder (ascl:1306.009), is a 6-Dimensional Friends-of-Friends (6D-FoF) phase space halo finder and constructs halo catalogs. The code uses using MPI and OpenMP APIs and can be compiled as a library for on-the-fly halo finding within an N-body/hydrodynamnical code. There is an associated halo merger tree code TreeFrog (ascl:1911.021).
OrbWeaver extracts orbits from halo catalogs, enabling large statistical studies of their orbital parameters. The code is run in two stages. For the first run, a configuration file is used to modify orbit host selection and the region around orbit host used for the superset of orbiting halos. Each orbit host has a orbit forest (containing halos that passed within the region of interest); the code generates a pre-processed catalog which contains a superset of orbiting halo for each identified orbit host. The second run uses the file list generated in the first stage for the creation of the orbit catalog, which is the final output.
WhereWolf tracks (sub)haloes even if they have been lost by a halo finder in cosmological simulations and supplements halo catalogs such as VELOCIraptor (ascl:1911.020) with these recovered (sub)haloes. The code can improve measurements of the subhalo/halo mass function and present estimates of the distribution of radii at which subhaloes merge.
HLattice simulates scalar fields and gravity in the early universe. The code allows the user to select between symplectic integrators, descretization schemes, and metrics such as Minkowski or FRW backgrounds and adaptice schemes in an "all-in-one" configuration file.
CLUSTEREASY is a parallel programming extension of the simulation program LATTICEEASY (ascl:1911.015); running the program in parallel greatly extends the range of scales and times that can be simulated. The program is particularly useful for the study of reheating and thermalization after inflation.
LATTICEEASY creates lattice simulations of the evolution of interacting scalar fields in an expanding universe. The program can do runs with different parameters and new models can be easily introduced for evaluation. Simulations can be done in one, two, or three dimensions by resetting a single variable. Mathematica notebooks for plotting the output and a range of models are also available for download; a parallel processing version of LATTICEEASY called CLUSTEREASY (ascl:1911.016) is also available.
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.
ATLAS performs the tracking, projecting, power-spectrum-making, and ring-fitting needed to turn a set of Dopplergram images into a set of frequency shift measurements. This code is essentially a combination of three codes, FRACK (FORTRAN Tracking), PSPEC (Power SPECtrum), and MRF (Multi-Ridge Fitting), included in the ATLAS package. ATLAS reads in a list of longitude/latitude coordinates corresponding to the desired tile centers and a set of full-disk Dopplergram images and outputs frequency shift measurements from each wave mode of each tile. The code relies on both distributed-memory (MPI) and shared-memory (OpenMP) parallelism to scale up to around 1000 processes. Due to the immense volume of data produced by the tracking and projecting steps, the intermediate data products (tiles, power spectra) are never written out.
Zeltron is an explicit 3D relativistic electromagnetic Particle-In-Cell code suited for modeling particle acceleration in astrophysical plasmas. The code is efficiently parallelized with the Message Passing Interface, and can be run on a laptop computer or on multiple cores on current supercomputers. Zeltron takes into account the effect of the radiation reaction force on the motion of the particles; it assigns variable weights to the macro-particles to model particle density gradients, and does not strictly conserve the total energy. The code uses linear interpolation to deposit the charges and currents generated by each particle at the nodes of the computational grid, and computes the charge and current densities for Maxwell's equations. Zeltron contains a large set of analysis tools, including plasma density, particle spectrum, optically thin synchrotron and inverse Compton spectra, angular distributions, and stress-energy tensor.
IDG (Image Domain Gridding) is an imager that makes w-term corrections and a-term corrections computationally very cheap. It works with WSClean (ascl:1408.023) and supports the same cleaning and data selections options that WSClean offers in normal mode (such as cotton-schwab, multi-frequency multi-scale cleaning, and auto-masking). IDG also allows gridding with a time-variable beam including the LOFAR, AARTFAAC and MWA beam and can perform full beam or differential correction. The code requires measurement sets with four polarizations (e.g. XX/XY/YX/YY), can apply a spatially varying time-variable TEC term that can additionally be different for different antennas and output channels, and performs extremely well on GPUs.
Fruitbat estimates the redshift of Fast Radio Bursts (FRB) from their dispersion measure. The code combines various dispersion measure (DM) and redshift relations with the YMW16 galactic dispersion measure model into a single easy to use API.
frbpoppy conducts fast radio burst population synthesis and continues the work of PSRPOP (ascl:1107.019) and PsrPopPy (ascl:1501.006) in the realm of FRBs. The code replicates observed FRB detection rates and FRB distributions in three steps. It first simulates a cosmic population of one-off FRBs and allows the user to select options such as models for source number density, cosmology, DM host/IGM/Milky Way, luminosity functions, and emission bands as well as maximum redshift and size of the FRB population. The code then generates a survey by adopting a beam pattern using various survey parameters, among them telescope gain, sampling time, receiver temperature, central frequency, channel bandwidth, number of polarizations, and survey region limits. Finally, frbpoppy convolves the generated intrinsic population with the generated survey to simulate an observed FRB population.
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