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Combining Maximum-Likelihood with Deep Learning for Event Reconstruction in IceCube

Hünnefeld, M. ; Abbasi, R.; Ackermann, M.; Adams, J.; Aguilar, J. A.; Ahlers, M.; Ahrens, M.; Alispach, C.; Alves, Jr, A.A. 1; Amin, N. M.; An, R.; Andeen, K.; Anderson, T.; Anton, G.; Argüelles, C.; Ashida, Y.; Axani, S.; Bai, X.; Balagopal, A. V.; ... mehr


The field of deep learning has become increasingly important for particle physics experiments, yielding a multitude of advances, predominantly in event classification and reconstruction tasks. Many of these applications have been adopted from other domains. However, data in the field of physics are unique in the context of machine learning, insofar as their generation process and the laws and symmetries they abide by are usually well understood. Most commonly used deep learning architectures fail at utilizing this available information. In contrast, more traditional likelihood-based methods are capable of exploiting domain knowledge, but they are often limited by computational complexity.
In this contribution, a hybrid approach is presented that utilizes generative neural networks to approximate the likelihood, which may then be used in a traditional maximum-likelihood setting. Domain knowledge, such as invariances and detector characteristics, can easily be incorporated in this approach. The hybrid approach is illustrated by the example of event reconstruction in IceCube.

Verlagsausgabe §
DOI: 10.5445/IR/1000154790
Veröffentlicht am 27.01.2023
DOI: 10.22323/1.395.1065
Zitationen: 1
Zitationen: 7
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Astroteilchenphysik (IAP)
Institut für Experimentelle Teilchenphysik (ETP)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 18.03.2022
Sprache Englisch
Identifikator ISSN: 1824-8039
KITopen-ID: 1000154790
Erschienen in Proceedings of 37th International Cosmic Ray Conference — PoS(ICRC2021), Ed.: A. Kappes
Veranstaltung 37th International Cosmic Ray Conference (ICRC 2021), Online, 12.07.2021 – 23.07.2021
Verlag Scuola Internazionale Superiore di Studi Avanzati (SISSA)
Seiten Art.-Nr.: 1065
Serie Pos proceedings of science ; 395
Vorab online veröffentlicht am 26.07.2021
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