Speaker
Matthew Buckley
(Rutgers University)
Description
I describe a new machine learning algorithm, Via Machinae, to identify cold stellar streams in data from the Gaia telescope. Via Machinae is based on ANODE, a general method that uses conditional density estimation and sideband interpolation to detect local overdensities in the data in a model agnostic way. By applying ANODE to the positions, proper motions, and photometry of stars observed by Gaia, Via Machinae obtains a collection of those stars deemed most likely to belong to a stellar stream. In this talk, I will provide an overview of the Via Machinae algorithm, using the known stream GD-1 as a worked example, and show preliminary results of our analysis across the full sky.
| Affiliation | Rutgers University |
|---|---|
| Academic Rank | Professor |
Author
Matthew Buckley
(Rutgers University)
Co-authors
David Shih
(Rutgers University)
Lina Necib
(California Institute of Technology)
John Tamanas
(University of California, Santa Cruz)
