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 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 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

2015 Nov 30

[ascl:1511.019] CosmoBolognaLib: Open source C++ libraries for cosmological calculations

CosmoBolognaLib contains numerical libraries for cosmological calculations; written in C++, it is intended to define a common numerical environment for cosmological investigations of the large-scale structure of the Universe. The software aids in handling real and simulated astronomical catalogs by measuring one-point, two-point and three-point statistics in configuration space and performing cosmological analyses. These open source libraries can be included in either C++ or Python codes.

[ascl:1511.018] LCD3: Three-parameter limb darkening coefficient sampling

LDC3 samples physically permissible limb darkening coefficients for the Sing et al. (2009) three-parameter law. It defines the physically permissible intensity profile as being everywhere-positive, monotonically decreasing from center to limb and having a curl at the limb. The approximate sampling method is analytic and thus very fast, reproducing physically permissible samples in 97.3% of random draws (high validity) and encompassing 94.4% of the physically permissible parameter volume (high completeness).

[ascl:1511.017] DES exposure checker: Dark Energy Survey image quality control crowdsourcer

DES exposure checker renders science-grade images directly to a web browser and allows users to mark problematic features from a set of predefined classes, thus allowing image quality control for the Dark Energy Survey to be crowdsourced through its web application. Users can also generate custom labels to help identify previously unknown problem classes; generated reports are fed back to hardware and software experts to help mitigate and eliminate recognized issues. These problem reports allow rapid correction of artifacts that otherwise may be too subtle or infrequent to be recognized.

2015 Nov 29

[ascl:1511.016] JKTLD: Limb darkening coefficients

JKTLD outputs theoretically-calculated limb darkening (LD) strengths for equations (LD laws) which predict the amount of LD as a function of the part of the star being observed. The coefficients of these laws are obtained by bilinear interpolation (in effective temperature and surface gravity) in published tables of coefficients calculated from stellar model atmospheres by several researchers. Many observations of stars require the strength of limb darkening (LD) to be estimated, which can be done using theoretical models of stellar atmospheres; JKTLD can help in these circumstances.

[ascl:1511.015] George: Gaussian Process regression

George is a fast and flexible library, implemented in C++ with Python bindings, for Gaussian Process regression useful for accounting for correlated noise in astronomical datasets, including those for transiting exoplanet discovery and characterization and stellar population modeling.

[ascl:1511.014] HumVI: Human Viewable Image creation

HumVI creates a composite color image from sets of input FITS files, following the Lupton et al (2004, ascl:1511.013) composition algorithm. Written in Python, it takes three FITS files as input and returns a color composite, color-saturated png image with an arcsinh stretch. HumVI reads the zero points out of the FITS headers and uses them to put all the images on the same flux scale; photometrically calibrated images produce the best results.

[ascl:1511.013] CCDtoRGB: RGB image production from three-band atronomical images

CCDtoRGB produces red‐green‐blue (RGB) composites from three‐band astronomical images, ensuring an object with a specified astronomical color has a unique color in the RGB image rather than burnt‐out white stars. Use of an arcsinh stretch shows faint objects while simultaneously preserving the structure of brighter objects in the field, such as the spiral arms of large galaxies.

[ascl:1511.012] milkywayproject_triggering: Correlation functions for two catalog datasets

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.

[ascl:1511.011] SparsePZ: Sparse Representation of Photometric Redshift PDFs

SparsePZ uses sparse basis representation to fully represent individual photometric redshift probability density functions (PDFs). This approach requires approximately half the parameters for the same multi-Gaussian fitting accuracy, and has the additional advantage that an entire PDF can be stored by using a 4-byte integer per basis function. Only 10-20 points per galaxy are needed to reconstruct both the individual PDFs and the ensemble redshift distribution, N(z), to an accuracy of 99.9 per cent when compared to the one built using the original PDFs computed with a resolution of δz = 0.01, reducing the required storage of 200 original values by a factor of 10-20. This basis representation can be directly extended to a cosmological analysis, thereby increasing computational performance without losing resolution or accuracy.

2015 Nov 28

[ascl:1511.010] Galileon-Solver: N-body code

Galileon-Solver adds an extra force to PMCode (ascl:9909.001) using a modified Poisson equation to provide a non-linearly transformed density field, with the operations all performed in real space. The code's implicit spherical top-hat assumption only works over fairly long distance averaging scales, where the coarse-grained picture it relies on is a good approximation of reality; it uses discrete Fourier transforms and cyclic reduction in the usual way.