Inferring astrophysical X-ray polarization with deep learning

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dc.contributor.authorMoriakov, Nikita
dc.contributor.authorSamudre, Ashwin
dc.contributor.authorNegro, Michela
dc.contributor.authorGieseke, Fabian
dc.contributor.authorOtten, Sydney
dc.contributor.authorHendriks, Luc
dc.date.accessioned2020-06-09T15:49:57Z
dc.date.available2020-06-09T15:49:57Z
dc.date.issued2020-05-16
dc.descriptionEighth Internation Conference on Learning Representations (ICRL 2020)en_US
dc.description.abstractWe 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.en_US
dc.description.sponsorshipWe want to thank the DarkMachines collaboration for bringing us together and for fruitful discussions. Michela Negro wants to acknowledge the IXPE team and in particular Niccoló Di Lalla and Alberto Manfreda for providing the simulated data samples.en_US
dc.description.urihttps://arxiv.org/abs/2005.08126en_US
dc.format.extent5 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/m2sfov-uo4v
dc.identifier.citationNikita Moriakov et al., Inferring astrophysical X-ray polarization with deep learning, https://arxiv.org/abs/2005.08126en_US
dc.identifier.urihttp://hdl.handle.net/11603/18845
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Center for Space Sciences and Technology
dc.relation.ispartofUMBC Physics Department
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.titleInferring astrophysical X-ray polarization with deep learningen_US
dc.typeTexten_US

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