starsml - Machine Learning for Astronomical Catalog Cross-Matching

The StarsML package addresses a fundamental challenge in modern astronomy: reliably cross-matching sources across different wavelength catalogs when traditional spatial-based methods fail. Developed by Victor Samuel Perez-Diaz for the Chandra-Gaia Catalog of Counterparts project, this package implements machine learning algorithms that go beyond simple positional matching to incorporate intrinsic object properties like magnitudes, colors, and distances.

The open-source repository can be found here: GitHub