Inferring astrophysical X-ray polarization with deep learning

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https://arxiv.org/abs/2005.08126

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http://hdl.handle.net/11603/18845

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UMBC Center for Space Sciences and Technology (CSST) / Center for Research and Exploration in Space Sciences & Technology II (CRSST II)
UMBC Faculty Collection
UMBC Physics Department

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Date

2020-05-16

Type of Work

conference papers and proceedings preprints
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Citation of Original Publication

Nikita Moriakov et al., Inferring astrophysical X-ray polarization with deep learning, https://arxiv.org/abs/2005.08126

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Subjects

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.