SKYCURTAINS: model-agnostic search for stellar streams with Gaia data

Abstract

We present SKYCURTAINS, a data-driven and model-agnostic method to search for stellar streams in the Milky Way galaxy using data from the Gaia telescope.SKYCURTAINS is a weakly supervised machine learning algorithm that builds a background enriched template in the signal region by leveraging the correlation of the source's characterizing features with their proper motion in the sky. The minimal model assumptions in the SKYCURTAINS method allow for a flexible and efficient search for various kinds of anomalies such as streams, globular clusters, or dwarf galaxies directly from the data. We test the performance of SKYCURTAINS on the GD-1 stream and show that it is able to recover the stream with a purity of 75.4 per cent which is an improvement of over 10 per cent over existing machine learning-based methods while retaining a signal efficiency of 37.9 per cent.