pyPETaL is a time-series data analysis pipeline for AGN reverberation mapping (RM) data. It combines multiple different popular softwares used for AGN RM analysis, including PyCCF, PyZDCF, JAVELIN, and PyROA, to produce time lag measurements. This pipeline also implements outlier rejection using Damped Random Walk Gaussian process fitting, and detrending through the LinMix algorithm. pyPetal implements a weighting scheme (for all lag-producing modules) in order to mitigate aliasing in peaks of time lag distributions between light curves.

pyPetal is very flexible, with almost every argument for each module allowing user input. pyPetal is designed to work with any combination of modules being run, allowing it to scale from the simplest to the most complex of projects.

JET (JWST Exoplanet Targeting) optimizes lists of exoplanet targets for atmospheric characterization by the James Webb Space Telescope (JWST). The software uses catalogs of planet detections, either simulated, or actual and categorizes targets by radius and equilibrium temperature; it also estimates planet masses and generates model spectra and simulated instrument spectra. JET then performs a statistical analysis to determine if the instrument spectra can confirm an atmospheric detection and finally ranks the targets within each category by observation time required for detection.

FALCO (Fast Linearized Coronagraph Optimizer) performs coronagraphic focal plane wavefront correction. It includes routines for pair-wise probing estimation of the complex electric field and Electric Field Conjugation (EFC) control. FALCO utilizes and builds upon PROPER (ascl:1405.006) and rapidly computes the linearized response matrix for each DM, which facilitates re-linearization after each control step for faster DM-integrated coronagraph design and wavefront correction experiments. A MATLAB implementation of FALCO (ascl:2304.004) is also available.

FALCO (Fast Linearized Coronagraph Optimizer) performs coronagraphic focal plane wavefront correction. It includes routines for pair-wise probing estimation of the complex electric field and Electric Field Conjugation (EFC) control. FALCO utilizes and builds upon PROPER (ascl:1405.006) and rapidly computes the linearized response matrix for each DM, which facilitates re-linearization after each control step for faster DM-integrated coronagraph design and wavefront correction experiments. A Python 3 implementation of FALCO (ascl:2304.005) is also available.

The code is aimed at the single-image deconvolution of astronomical images with a known Point Spread Function. The Scaled Gradient Projection (SGP) algorithm was originally written in MATLAB as part of the SGP-dec software (https://www.unife.it/prin/software/SGPdec_doc.pdf). Our code provides (1) a Python re-implementation of the SGP-dec software's MATLAB code, and (2) a generalization of the SGP code using beta divergence as the loss function.

BatAnalysis analyzes BAT Survey data. This code downloads BAT survey data, batch processes the survey observations, and extracts light curves and spectra for each survey observation for a given source. BatAnalysis can also create mosaicked images at different time bins and extract light curves and spectra from the mosaicked images for a given source.

Applefy calculates detection limits for exoplanet high contrast imaging (HCI) datasets. The package provides a number of features and functionalities to improve the accuracy and robustness of contrast curve calculations. Applefy implements the classical approach based on the t-test as well as the parametric boostrap test for non-Gaussian residual noise. Written in Python, it computes contrast curves and contrast grids.

ASSIST uses REBOUND's (ascl:1110.016) IAS15 integrator to integrate test particles trajectories in the field of the Sun, Moon, planets, and massive asteroids. The positions of the masses come from the JPL DE441 ephemeris and its associated asteroid perturber file. ASSIST incorporates the most significant gravitational harmonics and general relativistic corrections and accounts for position- and velocity-dependent non-gravitational effects. All components in the equations of motion have been verified to machine precision in a term-by-term comparison with output from JPL's small body integrator, and the first order variational equations are included for all terms to support orbit fitting and covariance mapping.

HaloGraphNet predicts halo masses from simulations using Graph Neural Networks. Given a dark matter halo and its galaxies, this software creates a graph with information about the 3D position, stellar mass and other properties. It then trains a Graph Neural Network to predict the mass of the host halo. Data are taken from the CAMELS hydrodynamic simulations.

pulsar_spectra provides a pulsar flux density catalog and automated spectral fitting software for finding spectral models. The package can also produce publication-quality plots and allows users to add new spectral measurements to the catalog. The spectral fitting software uses robust statistical methods to determine the best-fitting model for individual pulsar spectra.