Via Machinae: Searching for Stellar Streams using Unsupervised Machine Learning

Understand

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.