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Studies of Gamma-Ray Shower Reconstruction UsingDeep Learning
Linnaeus University, Faculty of Technology, Department of Physics and Electrical Engineering. (DISA-AP)ORCID iD: 0000-0003-2946-1313
Linnaeus University, Faculty of Technology, Department of Physics and Electrical Engineering.
Linnaeus University, Faculty of Technology, Department of Physics and Electrical Engineering. (DISA-AP)
Linnaeus University, Faculty of Technology, Department of Physics and Electrical Engineering. (DISA-AP)ORCID iD: 0000-0002-2115-2930
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2021 (English)In: Proceedings of Science: 37th International Cosmic Ray Conference (ICRC 2021), July 12th – 23rd, 2021 Online – Berlin, German, 2021, article id 758Conference paper, Published paper (Refereed)
Abstract [en]

The ALTO project aims to build a particle detector array for very high energy gamma ray observations optimized for soft spectrum sources. The accurate reconstruction of gamma ray events, in particular their energies, using a surface array is an especially challenging problem at the low energies ALTO aims to optimize for. In this contribution, we leverage Convolutional Neural Networks (CNNs) to improve reconstruction performance at lower energies ( smaller 1 TeV ) as compared to the SEMLA analysis procedure, which is a more traditional method using mainly manually derived features.rnWe present performance figures using different network architectures and training settings, both in terms of accuracy and training time, as well as the impact of various data augmentation techniques.

Place, publisher, year, edition, pages
2021. article id 758
Series
Proceedings of Science (PoS), E-ISSN 1824-8039
National Category
Astronomy, Astrophysics and Cosmology
Research subject
Physics, Astroparticle Physics
Identifiers
URN: urn:nbn:se:lnu:diva-110661Scopus ID: 2-s2.0-85144130601OAI: oai:DiVA.org:lnu-110661DiVA, id: diva2:1641386
Conference
37th International Cosmic Ray Conference (ICRC 2021), July 12th – 23rd, 2021 Online – Berlin, German
Available from: 2022-03-01 Created: 2022-03-01 Last updated: 2023-06-28Bibliographically approved

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Bylund, TomasKukec Mezek, GasperSenniappan, MohanrajBecherini, YvonnePunch, MichaelThoudam, Satyendra

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Bylund, TomasKukec Mezek, GasperSenniappan, MohanrajBecherini, YvonnePunch, MichaelThoudam, Satyendra
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Department of Physics and Electrical Engineering
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