SkyCURTAINs: Model agnostic search for Stellar Streams with Gaia data

TL;DR

SkyCURTAINs is a flexible, model-agnostic machine learning method that effectively detects stellar streams in Gaia data by reducing false positives and improving recovery accuracy compared to existing techniques.

Contribution

The paper introduces SkyCURTAINs, a novel weakly supervised, model-agnostic algorithm for detecting stellar streams using Gaia data, enhancing background modeling and detection accuracy.

Findings

Achieves 75.4% purity in recovering GD-1 stream.

Improves detection purity by over 10% compared to existing methods.

Retains a signal efficiency of 37.9%.

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 characterising features with their proper motion in the sky. This allows for a more representative template of the background in the signal region, and reduces the false positives in the search for stellar streams. 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% which is an improvement of over 10% over existing machine learning based methods while retaining a signal efficiency of 37.9%.