Astrophysics Source Code Library

Making codes discoverable since 1999

Welcome to the ASCL

The Astrophysics Source Code Library (ASCL) is a free online registry for source codes of interest to astronomers and astrophysicists, including solar system astronomers, and lists codes that have been used in research that has appeared in, or been submitted to, peer-reviewed publications. The ASCL is indexed by the SAO/NASA Astrophysics Data System (ADS) and Web of Science and is citable by using the unique ascl ID assigned to each code. The ascl ID can be used to link to the code entry by prefacing the number with (i.e.,

Most Recently Added Codes

2020 Jan 16

[submitted] JetSeT: numerical modeling and SED fitting tool for relativistic jets

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
sampling (emcee)

- Self-consistent temporal evolution of the plasma under the effect of radiative and accelerative processes,
both first order and second order (stochastic acceleration) processes.

2020 Jan 14

[submitted] SDSS Dual Active Nuclei Galaxy Detection Pipeline

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.

2020 Jan 10

[submitted] K2CE: Kepler-K2 Cadence Events

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 .

2019 Dec 31

[ascl:1912.020] MRExo: Non-parametric mass-radius relationship for exoplanets

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.

[ascl:1912.019] STACKER: Stack sources in interferometric data

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.

[ascl:1912.018] Tangos: Framework and web interface for database-driven analysis of numerical structure formation simulations

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.

[ascl:1912.017] PTMCMCSampler: Parallel tempering MCMC sampler package written in Python

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.

[ascl:1912.016] GWpy: Python package for studying data from gravitational-wave detectors

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.

[ascl:1912.015] ENTERPRISE: Enhanced Numerical Toolbox Enabling a Robust PulsaR Inference SuitE

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.

[ascl:1912.014] HARMPI: 3D massively parallel general relativictic MHD code

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.