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arXiv 2005.08126astro-ph.IMastro-ph.HEcs.LG
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Inferring astrophysical X-ray polarization with deep learning

Radboud University Medical Center(拉德堡德大学医学中心);Aix-Marseille-Univ(艾克斯-马赛大学);CNRS(法国国家科学研究中心);CINAM(分子与生物材料交叉研究中心);NASA-GSFC(美国国家航空航天局戈达德太空飞行中心);CRESST-UMBC(马里兰大学巴尔的摩县分校空间研究与探索中心);University of Copenhagen(哥本哈根大学);University of Amsterdam(阿姆斯特丹大学);Radboud University Nijmegen(拉德堡德大学奈梅亨分校);Radboud University(拉德堡德大学)

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

英文摘要:

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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