The Naval Observatory NOVAS C astrometry library, made better

A fairly comprehensive collection of accurate astronomical algorithms in JavaScript (TypeScript).

Astrarium — open-source planetarium software for Windows

A python DS9 extension for quicklook processing of astronomical images. This highly interactive extension can be generalized automatically to a set of images to turn the plug-in into a real multi-processing pipeline.

Galaxy Line Emission & Absorption Modeling

Real time meteor detection by deep learning

A Python package for manipulating and correcting variable point spread functions.

pySAS is a python wrapper for the Science Analysis Software (SAS) used for analyzing XMM-Newton data. This repository is maintained by the XMM-Newton Guest Observer Facility (GOF) at Goddard Space Flight Center for testing new pySAS functionality.

SUPPNet: Neural network for stellar spectrum normalisation

Pico-Planetarium is a compact, low-cost astronomy viewer built around the Raspberry Pi Pico 2W and a 480x320 ST7796 TFT display.

DIY project to build a motorized telescope cover, flat panel, or a combined flip-flat system.

Simulate time-variable granulation signatures in stellar spectra

Stellar aperture photometry

A set of tools for use by MAST community contributors preparing High Level Science Products (HLSP) or MAST Community Contributed Missions (MCCM) data collections. Currently, "Filename Check" is available, and "Metadata Check" will be coming soon.

Bayesian model reconstruction based on astronomical spectral line observations.

SEDBYS: A python-based SED Builder for Young Stars. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711021000327

Machine Learning pipeline to classify astronomical sources into galaxies, quasars and stars using photometric data.

Repository of pAstroCORE software.

Multi-spectral faint emission detection and color extraction tool.

Fast neural posterior estimation for gravitational wave events using normalizing flows efficiently infers high-dimensional GW parameters from waveform data.