Via Machinae: Searching for Stellar Streams using Unsupervised Machine Learning

TL;DR

Via Machinae is a novel unsupervised machine learning algorithm that detects stellar streams in Gaia data without relying on astrophysical assumptions, demonstrated on the GD-1 stream.

Contribution

The paper introduces Via Machinae, a new model-agnostic algorithm combining ANODE and Hough transform to identify stellar streams in Gaia data.

Findings

Successfully identified the GD-1 stream

Demonstrated flexibility to detect various stellar structures

Does not depend on astrophysical models

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 dataset, for example debris flow and globular clusters.