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SkyCURTAINs: Model agnostic search for Stellar Streams with Gaia data

Published inMonthly notices of the Royal Astronomical Society, vol. 536, no. 2, p. 1104-1111; stae2570
Publication date2025-01-01
First online date2024-11-20
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. 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%.

Citation (ISO format)
SENGUPTA, Debajyoti et al. SkyCURTAINs: Model agnostic search for Stellar Streams with Gaia data. In: Monthly notices of the Royal Astronomical Society, 2025, vol. 536, n° 2, p. 1104–1111. doi: 10.1093/mnras/stae2570
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Journal ISSN0035-8711
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