samuelperezdi/umlcaxs
Final repository for the project "Unsupervised Machine Learning for the Classification of Astrophysical X-ray Sources". V. S. Pérez-Díaz, J. R. Martínez-Galarza, A. Caicedo, R. D'Abrusco.
This project helps astronomers automatically categorize X-ray detections from compiled catalogs. It takes X-ray source data (like that from the Chandra Source Catalog) and outputs probabilistic classifications, indicating the likelihood of different astrophysical source types. This is for astronomers and astrophysicists who need to efficiently classify X-ray sources without relying on optical data or extensive pre-labeled training sets.
No commits in the last 6 months.
Use this if you need to classify X-ray sources for population studies, anomaly detection, or to understand individual objects, especially when optical counterparts or extensive labeled training data are scarce.
Not ideal if you require classifications based on a comprehensive set of multi-wavelength data including optical and infrared, or if you already have robust labeled training data for your specific classification task.
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Jan 23, 2024
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