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 ascl.net (i.e., ascl.net/1201.001).
Molsoft operates, monitors and schedules observations, both through predetermined schedule files and fully dynamically, at the refurbished Molonglo Observatory Synthesis Radio Telescope (MOST). It was developed as part of the UTMOST upgrade of the facility.
Astro-SCRAPPY detects cosmic rays in images (numpy arrays), based on Pieter van Dokkum's L.A.Cosmic algorithm and originally adapted from cosmics.py written by Malte Tewes. This implementation is optimized for speed, resulting in slight difference from the original code, such as automatic recognition of saturated stars (rather than treating such stars as large cosmic rays, and use of a separable median filter instead of the true median filter. Astro-SCRAPPY is an AstroPy (ascl:1304.002) affiliated package.
MGB (Marxist Ghost Buster) attacks spectral classification by using an interactive comparison with spectral libraries. It allows the user to move along the two traditional dimensions of spectral classification (spectral subtype and luminosity classification) plus the two additional ones of rotation index and spectral peculiarities. Double-lined spectroscopic binaries can also be fitted using a combination of two standards. The code includes OB2500 v2.0, a standard grid of blue-violet R ~ 2500 spectra of O stars from the Galactic O-Star Spectroscopic Survey, but other grids can be added to MGB.
Wōtan provides free and open source algorithms to remove trends from time-series data automatically as an aid to to search efficiently for transits in stellar light curves from surveys. The toolkit helps determine empirically the best tool for a given job, serving as a one-stop solution for various smoothing tasks.
XDF-GAN generates mock galaxy surveys with a Spatial Generative Adversarial Network (SGAN)-like architecture. Mock galaxy surveys are generated from data that is preprocessed as little as possible (preprocessing is only a 99.99th percentile clipping). The outputs can also be tessellated together to create a very large survey, limited in size only by the RAM of the generation machine.
ROHSA (Regularized Optimization for Hyper-Spectral Analysis) reveals the statistical properties of interstellar gas through atomic and molecular lines. It uses a Gaussian decomposition algorithm based on a multi-resolution process from coarse to fine grid to decompose any kind of hyper-spectral observations into a sum of coherent Gaussian. Optimization is performed on the whole data cube at once to obtain a solution with spatially smooth parameters.
intensitypower measures and models the auto- and cross-power spectrum multipoles of galaxy catalogs and radio intensity maps presented in spherical coordinates. It can also convert the multipoles to power spectrum wedges P(k,mu) and 2D power spectra P(k_perp,k_par). The code assumes the galaxy catalog is a set of discrete points and the radio intensity map is a pixelized continuous field which includes angular pixelization using healpix, binning in redshift channels, smoothing by a Gaussian telescope beam, and the addition of a Gaussian noise in each cell. The galaxy catalog and radio intensity map are transferred onto an FFT grid, and power spectrum multipoles are measured including curved-sky effects. Both maps include redshift-space distortions.
MCRGNet (Morphological Classification of Radio Galaxy Network) classifies radio galaxies of different morphologies. It is based on the Convolutional Neural Network (CNN), which is trained and applied under a three-step framework: 1.) pretraining the network unsupervisedly with unlabeled samples, 2.) fine-tuning the pretrained network parameters supervisedly with labeled samples, and 3.) classifying a new radio galaxy by the trained network. The code uses a dichotomous tree classifier composed of cascaded CNN based subclassifiers.
GIST (Galaxy IFU Spectroscopy Tool) provides a convenient all-in-one framework for the scientific analysis of fully reduced, (integral-field) spectroscopic data, conducting all the steps from the preparation of input data to the scientific analysis and to the production of publication-quality plots. In its basic set-up, the GIST pipeline extracts stellar kinematics, performs an emission-line analysis, and derives stellar population properties from full spectral fitting and via the measurement of absorption line-strength indices by exploiting pPXF (ascl:1210.002)and GandALF routines. The pipeline is not specific to any instrument or analysis technique, and includes a dedicated visualization routine with a sophisticated graphical user interface for fully interactive plotting of all measurements, spectra, fits, and residuals, as well as star formation histories and the weight distribution of the models.
Skyfield computes positions for the stars, planets, and satellites in orbit around the Earth. Its results should agree with the positions generated by the United States Naval Observatory and their Astronomical Almanac to within 0.0005 arcseconds (which equals half a “mas” or milliarcsecond). It computes geocentric coordinates or topocentric coordinates specific to your location on the Earth’s surface. Skyfield accepts AstroPy (ascl:1304.002) time objects as input and can return results in native AstroPy units but is not dependend on AstroPy nor its compiled libraries.