2020/10/24 by Joshua Yao-Yu Lin, Hang Yu, Lin, Joshua Yao-Yu +9
Engineering · Physics and Astronomy · #CCD and CMOS Imaging Sensors #Computational Physics (physics.comp-ph) #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences #Galaxies: Formation, Evolution, Phenomena #Stellar, planetary, and galactic studies #astro-ph.CO #physics.comp-ph
paper · pdf · doi:10.48550/arxiv.2010.12960
Accepted by Machine Learning and the Physical Sciences Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS), 2019
openalex publication_date 2020/10/24 · arxiv created 2020/10/27 · arxiv updated 2020/10/28 · openalex created_date 2020/10/29 · openalex updated_date 2026/07/28
Dark matter substructures are interesting since they can reveal the properties of dark matter. Collisionless N-body simulations of cold dark matter show more substructures compared with the population of dwarf galaxy satellites observed in our local group. Therefore, understanding the population and property of subhalos at cosmological scale would be an interesting test for cold dark matter. In recent years, it has become possible to detect individual dark matter subhalos near images of strongly lensed extended background galaxies. In this work, we discuss the possibility of using deep neural networks to detect dark matter subhalos, and showing some preliminary results with simulated data. We found that neural networks not only show promising results on detecting multiple dark matter subhalos, but also learn to reject the subhalos on the lensing arc of a smooth lens where there is no subhalo.