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Michela Negro

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Inferring astrophysical X-ray polarization with deep learning

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May 16, 2020
Nikita Moriakov, Ashwin Samudre, Michela Negro, Fabian Gieseke, Sydney Otten, Luc Hendriks

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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.

* Accepted to International Conference on Learning Representations (ICLR) 2020 Workshop: Fundamental Science in the Era of AI 
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