Machine Learning the Sixth Dimension: Stellar Radial Velocities from 5D Phase-space Correlations

Dropulic, Adriana and Ostdiek, Bryan and Chang, Laura J. and Liu, Hongwan and Cohen, Timothy and Lisanti, Mariangela (2021) Machine Learning the Sixth Dimension: Stellar Radial Velocities from 5D Phase-space Correlations. The Astrophysical Journal Letters, 915 (1). L14. ISSN 2041-8205

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Abstract

The Gaia satellite will observe the positions and velocities of over a billion Milky Way stars. In the early data releases, the majority of observed stars do not have complete 6D phase-space information. In this Letter, we demonstrate the ability to infer the missing line-of-sight velocities until more spectroscopic observations become available. We utilize a novel neural network architecture that, after being trained on a subset of data with complete phase-space information, takes in a star's 5D astrometry (angular coordinates, proper motions, and parallax) and outputs a predicted line-of-sight velocity with an associated uncertainty. Working with a mock Gaia catalog, we show that the network can successfully recover the distributions and correlations of each velocity component for stars that fall within ∼5 kpc of the Sun. We also demonstrate that the network can accurately reconstruct the velocity distribution of a kinematic substructure in the stellar halo that is spatially uniform, even when it comprises a small fraction of the total star count.

Item Type: Article
Subjects: STM Open Press > Physics and Astronomy
Depositing User: Unnamed user with email support@stmopenpress.com
Date Deposited: 09 May 2023 06:16
Last Modified: 18 Jun 2024 07:12
URI: http://journal.submissionpages.com/id/eprint/1196

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