2024/05/20 by Debajyoti Sengupta, Sengupta, Debajyoti, Stephen Mulligan +7 · 1 citation
Computer Science · #Advanced Computational Techniques and Applications #Astrophysics of Galaxies (astro-ph.GA) #Data Analysis #FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #Statistics and Probability (physics.data-an) #Time Series Analysis and Forecasting
paper · doi:10.48550/arxiv.2405.12131
openalex publication_date 2024/05/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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%.