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 ascl.net (i.e., ascl.net/1201.001).
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.
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.
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.
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.
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.
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.
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.
We have developed a crowdsourcing web application for image quality control employed by the Dark Energy Survey. Dubbed the "DES exposure checker", it renders science-grade images directly to a web browser and allows users to mark problematic features from a set of predefined classes. Users can also generate custom labels and thus help identify previously unknown problem classes. User reports are fed back to hardware and software experts to help mitigate and eliminate recognized issues. We report on the implementation of the application and our experience with its over 100 users, the majority of which are professional or prospective astronomers but not data management experts. We discuss aspects of user training and engagement, and demonstrate how problem reports have been pivotal to rapidly correct artifacts which would likely have been too subtle or infrequent to be recognized oth- erwise. We conclude with a number of important lessons learned, suggest possible improvements, and recommend this collective exploratory approach for future astronomical surveys or other extensive data sets with a sufficiently large user base. We also release open-source code of the web application and host an online demo version at http://des-exp-checker.pmelchior.net.
ALFA fits emission line spectra of arbitrary wavelength coverage and resolution, fully automatically. It uses a catalogue of lines which may be present to construct synthetic spectra, the parameters of which are then optimised by means of a genetic algorithm. Uncertainties are estimated using the noise structure of the residuals. An emission line spectrum containing several hundred lines can be fitted in a few seconds using a single processor of a typical contemporary desktop or laptop PC.
T-PHOT extracts accurate photometry from low-resolution images of extragalactic fields, where the blending of sources can be a serious problem for accurate and unbiased measurement of fluxes and colors. It gathers data from a high-resolution image of a region of the sky and uses the source positions and morphologies to obtain priors for the photometric analysis of the lower resolution image of the same field. T-PHOT handles different types of datasets as input priors, including a list of objects that will be used to obtain cutouts from the real high-resolution image, a set of analytical models (as .fits stamps), and a list of unresolved, point-like sources, useful for example for far-infrared wavelength domains. T-PHOT yields accurate estimations of fluxes within the intrinsic uncertainties of the method when systematic errors are taken into account (which can be done using a flagging code given in the output). T-PHOT is many times faster than similar codes like TFIT and CONVPHOT and works well for the analysis of large datasets; it handles multiwavelength optical to far-infrared image photometry. T-PHOT was developed as part of the ASTRODEEP project (www.astrodeep.eu).