We introduce GalMOSS, a Python-based, Torch-powered tool for two-dimensional fitting of galaxy profiles. By seamlessly enabling GPU parallelization, GalMOSS meets the high computational demands of large-scale galaxy surveys, placing galaxy profile fitting in the LSST-era. It incorporates widely used profiles such as the Sérsic, Exponential disk, Ferrer, King, Gaussian, and Moffat profiles, and allows for the easy integration of more complex models. Tested on 8,289 galaxies from the Sloan Digital Sky Survey (SDSS) g-band with a single NVIDIA A100 GPU, GalMOSS completed classical Sérsic profile fitting in about 10 minutes. Benchmark tests show that GalMOSS achieves computational speeds that are 6 $\times$ faster than those of default implementations.

STAR SHADOW is a Python code that is aimed at the automatic analysis of space based light curves of eclipsing binaries and provide a measurement of eccentricity, among other parameters. It provides a recipe to measure timings of eclipses using the time derivatives of the light curves, using a model of orbital harmonics obtained from an initial iterative prewhitening of sinusoids. Since the algorithm extracts the harmonics from the rest of the sinusoidal variability eclipse timings can be measured even in the presence of other (astrophysical) signals. The aim is to determine the orbital eccentricity automatically from the light curve along with information about the other variability present in the light curve. The output includes, but is not limited to, a sinusoid plus linear model of the light curve, the orbital period, the eccentricity, argument of periastron and inclination. See the documentation for more information.

ECLIPSR is a Python code that is aimed at the automatic analysis of space based light curves to find eclipsing binaries and provide some first order measurements, among others the binary star period and eclipse depths. It provides a recipe to find individual eclipses using the time derivatives of the light curves, with as primary benefit that eclipses can still be detected in light curves of stars where the dominating variability is for example pulsations. Since the algorithm detects each eclipse individually, even light curves containing only one eclipse can in principle be successfully analysed (and classified). The aim is to find eclipsing binaries among both pulsating and non-pulsating stars in a homogeneous and quick manner to be able to process large amounts of light curves in reasonable amounts of time. The output includes, but is not limited to, the individual eclipse markers, the period and time of first (primary) eclipse and a score between 0 and 1 indicating the likelihood that the analysed light curve is that of an eclipsing binary. See the documentation for more information on the discriminating power of this variable. After an update, a random forest classifier can now be used to arrive at a decision of 0 or 1 instead.

MGPT (Modified Gravity Perturbation Theory) computes 2-point statistics for LCDM model, DGP and Hu-Sawicky f(R) gravity. Written in C, the code can be easily modified to include other models. Specifically, it computes the SPT matter power spectrum, SPT Lagrangian-biased tracers power spectrum, and the CLPT matter correlation function. MGPT also computes the CLPT Lagrangian-biased tracers correlation function and a set of Q and R functionsfrom which other statistics, as leading order bispectrum, can be constructed.

CCBH-Numerics (previously called CCBH-PLPP) computes the probability of the existence of a single cosmologically coupled black hole (BH) with a formation mass below a specified threshold for given observational data of binary black holes (BBHs) from gravitational waves. The code uses the unbiased population of BBHs, as given by the power-law-plus-peak (PLPP) profile, as the observational input, and assumes that the detected BBHs are formed from stellar evolution, not primordial BHs. CCBH-Numerics also works with individual data from BBHs and for NSBH pairs as well.

Rwcs offers access to all the projection and distortion systems of WCSLIB (ascl:1108.003) in R. This can be used directly for, for example, pixel lookups, or for higher level general distortion and projection.

Rfits reads and writes FITS images, tables, and headers. Written in R, Rfits works with all types of compressed images, and both ASCII and binary tables. It uses CFITSIO (ascl:1010.001) for all low level FITS IO, so in general should be as fast as other CFITSIO-based software. For images, Rfits offers fully featured memory mapping and on-the-fly subsetting (by pixel and coordinate) and sparse pixel sampling, allowing for efficient inspection of very large (larger than memory) images.

NMMA probes nuclear physics and cosmology with multimessenger analysis. This fully featured, Bayesian multi-messenger pipeline targets joint analyses of gravitational-wave and electromagnetic data (focusing on the optical). Using bilby (ascl:1901.011) as the back-end, the software uses a variety of samplers to sampling these data sets. NMMA uses chiral effective field theory based neutron star equation of states when performing inference, and is also capable of estimating the Hubble Constant.

polarizationtools comprises different python3 scripts to convert, analyze, and simulate polarization data. (1) Convert Stokes parameters into linear polarization parameters with proper treatment of the uncertainties and vice versa. (2) Shift electric vector position angle (EVPA) data points in time series to account for the 180 degrees ambiguity. (3) Identify rotations of the EVPA e.g. in blazar polarization monitoring data according to various rotation definitions. (4) Simulate polarization time series as a random walk in the Stokes Q-U plane.

escatter.py performs Monte Carlo simulations of electron scattering events. The code was developed to better understand the emission lines from the interacting supernova SN 2021adxl, specifically the blue excess seen in the Hα 6563A emission line. escatter follows a photon that was formed in a thin interface between the supernova ejecta and surrounding material as it travels radially outwards through the dense material, scattering electrons outwards until it reaches an optically thin region, and plots a histogram of the emergent photons.