• Corpus ID: 249062764

Revealing the Milky Way's Most Recent Major Merger with a Gaia EDR3 Catalog of Machine-Learned Line-of-Sight Velocities

@inproceedings{Dropulic2022RevealingTM,
  title={Revealing the Milky Way's Most Recent Major Merger with a Gaia EDR3 Catalog of Machine-Learned Line-of-Sight Velocities},
  author={Adriana Dropulic and Hongwan Liu and Bryan Ostdiek and Mariangela Lisanti},
  year={2022}
}
Machine learning can play a powerful role in inferring missing line-of-sight velocities from astrometry in surveys such as Gaia . In this paper, we apply a neural network to Gaia Early Data Release 3 (EDR3) and obtain line-of-sight velocities and associated uncertainties for ∼ 92 million stars. The network, which takes as input a star’s parallax, angular coordinates, and proper motions, is trained and validated on ∼ 6 . 4 million stars in Gaia with complete phase-space information. The network… 

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