150 citations · 970 across the 31 of their papers we have counts for
5 papers · 1 filter
Revealing the Milky Way's Most Recent Major Merger with a Gaia EDR3 Catalog of Machine-Learned Line-of-Sight Velocities
Adriana Dropulic, Hongwan Liu, Bryan Ostdiek +1
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 Ea…
Machine Learning the 6th Dimension: Stellar Radial Velocities from 5D Phase-Space Correlations
Adriana Dropulic, Bryan Ostdiek, Laura J. Chang +3
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 p…
Chasing Accreted Structures within Gaia DR2 using Deep Learning
Lina Necib, Bryan Ostdiek, Mariangela Lisanti +3
In previous work, we developed a deep neural network classifier that only relies on phase-space information to obtain a catalog of accreted stars based on the second data release o…
Evidence for a Vast Prograde Stellar Stream in the Solar Vicinity
Lina Necib, Bryan Ostdiek, Mariangela Lisanti +6
Massive dwarf galaxies that merge with the Milky Way on prograde orbits can be dragged into the disk plane before being completely disrupted. Such mergers can contribute to an accr…
Cataloging Accreted Stars within Gaia DR2 using Deep Learning
Bryan Ostdiek, Lina Necib, Timothy Cohen +6
The goal of this study is to present the development of a machine learning based approach that utilizes phase space alone to separate the Gaia DR2 stars into two categories: those…