SkyCURTAINs: Model agnostic search for Stellar Streams with Gaia data

Article Article scientifique Article SkyCURTAINs: Model agnostic search for Stellar Streams with Gaia data eng 2024-11-20 2025-01-01 author Sengupta Debajyoti 0000-0003-2676-3498 938067 author Mulligan Stephen 0009-0006-0310-9548 417365 Département de physique nucléaire et corpusculaire (FINUC) author Shih David 0000-0003-3408-3871 author Raine Johnny 898599 author Golling Tobias 827102 Département de physique nucléaire et corpusculaire (FINUC) 1104-1111 stae2570 10.1093/mnras/stae2570 0035-8711 https://academic.oup.com/mnras/advance-article/doi/10.1093/mnras/stae2570/7905876 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%. Dewey 500.2 34 Département de physique nucléaire et corpusculaire 132 false Monthly notices of the Royal Astronomical Society 536 2 7173466b-5a37-47c6-a49a-99e638f98859 Article (Published version) Public stae2570.pdf 3299545 application/pdf CC BY-4.0 204ee880ea06adff23e25202b5434eeed0b56d09c1788052cd65c51a7ef58665 openAccess Faculté des sciences / Section de physique / Département de physique nucléaire et corpusculaire

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SkyCURTAINs: Model agnostic search for Stellar Streams with Gaia data	Monthly notices of the Royal Astronomical Society			2025	57	10

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Mulligan, Stephen