Inferring astrophysical X-ray polarization with deep learning

2020/05/16 by Nikita Moriakov, Moriakov, Nikita, Ashwin Samudre +9
Physics and Astronomy · #FOS: Computer and information sciences #FOS: Physical sciences #Gamma-ray bursts and supernovae #High Energy Astrophysical Phenomena (astro-ph.HE) #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Machine Learning (cs.LG) #Nuclear Physics and Applications #Particle Detector Development and Performance

paper · doi:10.48550/arxiv.2005.08126

openalex publication_date 2020/05/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We investigate the use of deep learning in the context of X-ray polarization detection from astrophysical sources as will be observed by the Imaging X-ray Polarimetry Explorer (IXPE), a future NASA selected space-based mission expected to be operative in 2021. In particular, we propose two models that can be used to estimate the impact point as well as the polarization direction of the incoming radiation. The results obtained show that data-driven approaches depict a promising alternative to the existing analytical approaches. We also discuss problems and challenges to be addressed in the near future.

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