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).
This code implements a hierarchical bayesian inference method for constraining the background cosmological history, in particular the Hubble constant, together with modified gravitational-wave propagation and binary black holes population models (mass, redshift and spin distributions) with gravitational-wave data. It includes support for loading and analysing data from the GWTC-3 catalog as well as for generating injections to evaluate selection effects, and features a module to run in parallel on clusters.
Eventdisplay is a reconstruction and analysis pipline for data of Imaging Atmospheric Cherenkov Telescopes (IACT). It has been primarily developed for VERITAS and CTA analysis.
The package consists of several analysis tools:
1. `evndisp`: calibrate and parametrize images, event reconstruction, stereo analysis
2. `trainTMVAforAngularReconstruction`: train boosted decision trees for direction and energy reconstruction
3. `mscw_energy`: fill and use lookup tables for mean scaled with and lenght calculation, energy reconstruction, stereo reconstruction
4. `trainTMVAforGammaHadronSeparation`: train boosted decision trees for gamma/hadron separation
5. `makeEffectiveArea`: calculation of the instrument response functions (effective areas, angular point-spread function, energy resolution)
6. `makeRadialAcceptance`: calculation of radial camera acceptance from data files
7. `anasum`: analysis to calculate sky maps and spectral energy distribution
8. `libVAnaSum`: shared library tools (to be used with [ROOT](https://root.cern/) to e.g., plot instrument response function, spectral energy distributions, light curves, sky maps
PyMCCF emulates a PASCAL program written by V.L.Oknyansky for use with reverberation mapping (RM). The code is updated version of the Gaskell & Spark (1986) method ICCF.
The code cross correlates two light curves that are unevenly sampled using linear interpolation and measures the peak and centroid of the cross-correlation function. The most
significant improvement of the code is using special parametor MAX which reduces the number of used interpolated pointes just to those whih not further from nearest real one than
the MAX. This way gives opportunities significantly reduce noise from interpolation errors. Also in ICCF were introduced constant values at the time space before the first
and after the last points of the sets. The number of interpolated pairs in the ICCF were forever the same for each time delay, but sure the signal/noise ratio drops down as the
time delay grows. In our code the number correlated pairs of data are not the same for each time delay. In the PyCCD version of the code published by Sun et al. also are used
different numbers of pair for each time delay due to not using any extrapolatation of the data sets, but in the version are used interpolated points in the big gaps
at data sets which are very common for astronomical data. So we were based on the PyCCF code with some correction which give us opportunity to introduce the new parameter MAX.
Additionally we preferer to interpolated just the best one from the 2 data sets used for RM. The method of getting errors for RM in our code PyMCCF were not changed
and is exactly the same as in PyCCF. So it is possible, in addition, to run Monto Carlo iterations using flux randomization and random subset selection (RSS) to produce
cross-correlation centroid distributions to estimate the uncertainties in the cross correlation results.
GWFAST is a Python code for forecasting the signal-to-noise ratios and parameter estimation capabilities of networks of gravitational-wave detectors, based on the Fisher information matrix approximation. It is designed for applications to third-generation gravitational-wave detectors.
It is based on Automatic Differentiation, which makes use of the library JAX. This allows efficient parallelization and numerical accuracy. The code includes a module for parallel computation on clusters.
EXCEED-DM (EXtended Calculation of Electronic Excitations for Direct detection of Dark Matter) provides a complete framework for computing DM-electron interaction rates. Given an electronic configuration, EXCEED-DM computes the relevant electronic matrix elements, then particle physics specific rates from these matrix elements. This allows for separation between approximations regarding the electronic state configuration, and the specific calculation being performed.
APERO (A PipelinE to Reduce Observations) performs data reduction for the Canada-France-Hawaii Telescope's near-infrared spectropolarimeter SPIRou and offers different recipes or modules for performing specific tasks. APERO can individually run recipes or process a set of files, such as cleaning a data file of detector effects, collecting all dark files and creating a master dark image to use for correction, and creating a bad pixel mask for identifying and dealing with bad pixels. It can extract out flat images to measure the blaze and produced blaze correction and flat correction images, extract dark frames to provide correction for the thermal background after extraction of science or calibration frames, and correct extracted files for leakage coming from a FP (for OBJ_FP files only). It can also take a hot star and calculate telluric transmission, and then use the telluric transmission to calculate principle components (PCA) for correcting input images of atmospheric absorption, among many other tasks.
ODNet uses a convolutional neural network to examine frames of a given observation, using the flux of a targeted star along time, to detect occultations. This is particularly useful to reliably detect asteroid occultations for the Unistellar Network, which consists of 10,000 digital telescopes owned by citizen scientists that is regularly used to record asteroid occultations. ODNet is not costly in term of computing power, opening the possibility for embedding the code on the telescope directly. ODNet's models were developed and trained using TensorFlow version 2.4.
BiGONLight (Bi-local geodesic operators framework for numerical light propagation) encodes the Bi-local Geodesic Operators formalism (BGO) to study light propagation in the geometric optics regime in General Relativity. The parallel transport equations, the optical tidal matrix, and the geodesic deviation equations for the bilocal operators are expressed in 3+1 form and encoded in BiGONLight as Mathematica functions. The bilocal operators are used to obtain all possible optical observables by combining them with the observer and emitter four-velocities and four-accelerations. The user can choose the position of the source and the observer anywhere along the null geodesic with any four-velocities and four-accelerations.
Korg computes stellar spectra from 1D model atmospheres and linelists assuming local thermodynamic equilibrium and implements both plane-parallel and spherical radiative transfer. The code is generally faster than other codes, and is compatible with automatic differentiation libraries and easily extensible, making it ideal for statistical inference and parameter estimation applied to large data sets.
H-FISTA (Hierarchical Fast Iterative Shrinkage Thresholding Algorithm) retrieves the phases of the wavefield from intensity measurements for pulsar spectroscopy. The code accepts input data in ASCII format as produced by PSRchive's (ascl:1105.014) psrflux function, a FITS file, or a pickle. If using a notebook, any custom reader can be used as long as the data ends up in a NumPy array. H-FISTA obtains sparse models of the wavefield in a hierarchical approach with progressively increasing depth. Once the tail of the noise distribution is reached, the hierarchy terminates with a final unregularized optimization, resulting in a fully dense model of the complex wavefield that permits the discovery of faint signals by appropriate averaging.