Gaia EDR3 Catalogs of Machine-Learned Radial Velocities

Gaia EDR3 Catalogs of Machine-Learned Radial Velocities Spatially complete Test-Set and Machine-Learned Radial Velocity (ML-RV) Catalogs described in Dropulic et al., arXiv:2205.12278. The spatially complete Test-Set Catalog contains a total of 4,332,657 stars, while the spatially complete ML-RV Catalog contains 91,840,346 stars. We provide Gaia EDR3 Source IDs, the network-predicted line-of-sight velocity in km/s, and the network-predicted uncertainty in km/s. We have included a simple Jupyter notebook demonstrating how to import the data, and make a simple histogram with it. If you find this catalog useful in your work, please cite Dropulic et al. arXiv:2205.12278, as well as Dropulic et al. ApJL 915, L14 (2021) arXiv:2103.14039.

Related Organizations

College of New Jersey
United States
Flatiron Institute
United States
The NSF AI Institute for Artificial Intelligence and Fundamental Interactions
United States
New York University
United States

Keywords

Machine Learning, Enceladus, Gaia EDR3, Substructure, Gaia, Radial Velocity, Gaia Sausage

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