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Title: via machinae : Searching for stellar streams using unsupervised machine learning

Journal Article · · Monthly Notices of the Royal Astronomical Society
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ABSTRACT We develop 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. We further apply an automated line-finding method based on the Hough transform to search for line-like features in patches of the sky. In this paper, we describe the via machinae algorithm in detail and demonstrate our approach on the prominent stream GD-1. Though some parts of the algorithm are tuned to increase sensitivity to cold streams, the via machinae technique itself does not rely on astrophysical assumptions, such as the potential of the Milky Way or stellar isochrones. This flexibility suggests that it may have further applications in identifying other anomalous structures within the Gaia data set, for example debris flow and globular clusters.

Research Organization:
California Institute of Technology (CalTech), Pasadena, CA (United States); Rutgers University, Piscataway, NJ (United States); University of California, Oakland, CA (United States)
Sponsoring Organization:
Sherman Fairchild Fellowship; USDOE; USDOE Office of Science (SC)
Grant/Contract Number:
AC02-05CH11231; SC0010008; SC0011632
OSTI ID:
1835951
Journal Information:
Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 4 Vol. 509; ISSN 0035-8711
Publisher:
Oxford University PressCopyright Statement
Country of Publication:
United Kingdom
Language:
English

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