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 Nov 23

2020 Nov 20

[submitted] GOTHIC : Detection of Double Nuclei Galaxies in SDSS

It is now well established that galaxy interactions and mergers play a crucial role in the hierarchical growth of structure in our universe. Galaxy mergers can lead to the formation of elliptical galaxies and larger disk galaxies, as well as drive galaxy evolution through star formation and nuclear activity. During mergers the nuclei of the individual galaxies come closer and finally form a double nuclei galaxy. Although mergers are common, the detection of double-nuclei galaxies (DNGs) is rare and fairly serendipitous. Their detection is very important as their properties can help us understand the the formation of supermassive black hole (SMBH) binaries, dual active galactic nuclei (DAGN) and the associated feedback effects. There is thus a need for an automatic/systematic survey of data for the discovery of double nuclei galaxies.

Using the Sloan digital sky survey (SDSS) as the target catalog, we have introduced a novel algorithm GOTHIC (Graph-bOosTed iterated HIll Climbing) that detects whether a given image of a galaxy has characteristic features of a DNG. We have tested the algorithm on a random sample of 100,000 galaxies from the Stripe 82 region in SDSS and obtained a maximum detection rate of 4.2 with a careful choice of the input catalog.

2020 Nov 17

[submitted] Data Processing Pipeline For Tianlai Experiment

The Tianlai project is a 21cm intensity mapping experiment aimed at detecting dark energy by measuring the baryon acoustic oscillation (BAO) features in the large scale structure power spectrum. This experiment provides an opportunity to test the data processing methods for cosmological 21cm signal extraction, which is still a great challenge in current radio astronomy research. The 21cm signal is much weaker than the foregrounds and easily affected by the imperfections in the instrumental responses. Furthermore, processing the large volumes of interferometer data poses a practical challenge. We have developed a data processing pipeline software called tlpipe to process the drift scan survey datafrom the Tianlai experiment. It performs offline data processing tasks such as radio frequency interference (RFI) flagging, array calibration, binning, and map-making, etc. It also includes utility functions needed for the data analysis, such as data selection, transformation, visualization and others. A number of new algorithms are implemented, for example the eigenvector decomposition method for array calibration and the Tikhnov regularization for m-mode analysis.

2020 Nov 12

[submitted] frbcat

Pip install-able Python package for querying Fast Radio Burst catalogues.

Can download FRB data from FRBCAT web page, the CHIME-REPEATERS web page and the Transient Name Server (TNS).

2020 Nov 09

[submitted] MUSE-PSFR

The MUSE-PSFR code allows to reconstruct a PSF for the MUSE WFM-AO mode, using telemetry data from SPARTA.

2020 Nov 01

[ascl:2011.005] DarkCapPy: Dark Matter Capture and Annihilation

DarkCapPy calculates rates associated with dark matter capture in the Earth, annihilation into light mediators, and observable decay of the light mediators near the surface of the Earth. This Python/Jupyter package can calculate the Sommerfeld enhancement at the center of the Earth and the timescale for capture-annihilation equilibrium, and can be modified for other compact astronomical objects and mediator spins.

[ascl:2011.004] MCMCDiagnostics: Markov Chain Monte Carlo convergence diagnostics

MCMCDiagnostics contains two diagnostics, written in Julia, for Markov Chain Monte Carlo. The first is potential_scale_reduction(chains...), which estimates the potential scale reduction factor, also known as Rhat, for multiple scalar chains
. The second, effective_sample_size(chain), calculates the effective sample size for scalar chains. These diagnostics are intended as building blocks for use by other libraries.

[ascl:2011.003] Kalkayotl: Inferring distances to stellar clusters from Gaia parallaxes

Kalkayotl obtains samples of the joint posterior distribution of cluster parameters and distances to the cluster stars from Gaia parallaxes using Bayesian inference. The code is designed to deal with the parallax spatial correlations of Gaia data, and can accommodate different values of parallax zero point and spatial correlation functions.

[ascl:2011.002] CAPTURE: Interferometric pipeline for image creation from GMRT data

CAPTURE (CAsa Pipeline-cum-Toolkit for Upgraded Giant Metrewave Radio Telescope data REduction) produces continuum images from radio interferometric data. Written in Python, it uses CASA (ascl:1107.013) tasks to analyze data obtained by the GMRT. It can produce self-calibrated images in a fully automatic mode or can run in steps to allow the data to be inspected throughout processing.

[ascl:2011.001] AdaMet: Adaptive Metropolis for Bayesian analysis

AdaMet (Adaptive Metropolis) performs efficient Bayesian analysis. The user-friendly Python package is an implementation of the Adaptive Metropolis algorithm. In many real-world applications, it is more efficient and robust than emcee (ascl:1303.002), which warm-up phase scales linearly with the number of walkers. For this reason, and because of its didactic value, the AdaMet code is provided as an alternative.