Via Machinae: Searching for Stellar Streams using Unsupervised Machine Learning

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.

17 pages, 17 figures, v2: references added, minor corrections, v3: published version

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Cited by in corpus (21)

• Classifying Anomalies THrough Outer Density Estimation (CATHODE)
• Online-compatible Unsupervised Non-resonant Anomaly Detection
• The Hough Stream Spotter: A New Method for Detecting Linear Structure in Resolved Stars and Application to the Stellar Halo of M31
• The Interplay of Machine Learning--based Resonant Anomaly Detection Methods
• Anomaly Detection under Coordinate Transformations
• High-dimensional and Permutation Invariant Anomaly Detection
• Measuring Galactic Dark Matter through Unsupervised Machine Learning
• Prospects for Detecting Gaps in Globular Cluster Stellar Streams in External Galaxies with the Nancy Grace Roman Space Telescope
• Hidden deep in the halo: selection of a reduced proper motion halo catalogue and mining retrograde streams in the velocity space
• How well can we unravel the accreted constituents of the Milky Way stellar halo? A test on cosmological hydrodynamical simulations
• High-dimensional Anomaly Detection with Radiative Return in $e^{+}{e}^{-}$ Collisions
• Exploring the ex-situ components within Gaia DR3
• Weakly-Supervised Anomaly Detection in the Milky Way
• Non-resonant Anomaly Detection with Background Extrapolation
• Simulation-based Anomaly Detection for Multileptons at the LHC
• GalaxyFlow: Upsampling Hydrodynamical Simulations for Realistic Mock Stellar Catalogs
• Sensitivity Estimation for Dark Matter Subhalos in Synthetic Gaia DR2 using Deep Learning
• Orbital phase-driven biases in Galactic mass constraints from stellar streams
• StarStream: Automatic detection algorithm for stellar streams
• Applying machine learning to Galactic Archaeology: how well can we recover the origin of stars in Milky Way-like galaxies?
• OmniLearned: A Foundation Model Framework for All Tasks Involving Jet Physics