The most comprehensive collection of accurate astronomical algorithms in JavaScript (TypeScript).
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Updated
May 5, 2025 - TypeScript
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The most comprehensive collection of accurate astronomical algorithms in JavaScript (TypeScript).
The Naval Observatory NOVAS C astrometry library, made better
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.
Astrarium — open-source planetarium software for Windows
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
Stellar aperture photometry
A testbed for stellar granulation in exoplanet RV searches.
Bayesian model reconstruction based on astronomical spectral line observations.
Pico-Planetarium is a compact, low-cost astronomy viewer built around the Raspberry Pi Pico 2W and a 480x320 ST7796 TFT display.
SEDBYS: A python-based SED Builder for Young Stars. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711021000327
Repository of pvCORE software
DIY project to build a motorized telescope cover, flat panel, or a combined flip-flat system.
A project implementing algorithms from "Practical Astronomy with your Calculator or Spreadsheet" for astronomical calculations and data analysis.
A C++ portage of pyphot for computing photometry from spectra
Multimodal Estimation Routine for the Cosmological Unravelling of Rest-frame Infrared Uniformised Spectra (MERCURIUS) uses the pymultinest package (Feroz et al. 2009; Buchner et al. 2014) to fit and plot greybody spectra given far-infrared (FIR) photometry of an object.
Machine Learning pipeline to classify astronomical sources into galaxies, quasars and stars using photometric data.
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