Inferring dark matter substructure with astrometric lensing beyond the power spectrum

Journal Article · 19 January 2022 · Machine Learning: Science and Technology

DOI: https://doi.org/10.1088/2632-2153/ac494a · OSTI ID:1841054

Mishra-Sharma, Siddharth

Abstract Astrometry—the precise measurement of positions and motions of celestial objects—has emerged as a promising avenue for characterizing the dark matter population in our Galaxy. By leveraging recent advances in simulation-based inference and neural network architectures, we introduce a novel method to search for global dark matter-induced gravitational lensing signatures in astrometric datasets. Our method based on neural likelihood-ratio estimation shows significantly enhanced sensitivity to a cold dark matter population and more favorable scaling with measurement noise compared to existing approaches based on two-point correlation statistics. We demonstrate the real-world viability of our method by showing it to be robust to non-trivial modeled as well as unmodeled noise features expected in astrometric measurements. This establishes machine learning as a powerful tool for characterizing dark matter using astrometric data.

• Sponsoring Organization: USDOE
• Grant/Contract Number: SC0012567
• OSTI ID: 1841054
• Journal Information: Machine Learning: Science and Technology, Journal Name: Machine Learning: Science and Technology Journal Issue: 1 Vol. 3; ISSN 2632-2153
• Publisher: IOP PublishingCopyright Statement
• Country of Publication: United Kingdom
• Language: English