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Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8

Esfahani, A Ashtari; Böser, S; Buzinsky, N; Cervantes, R; Claessens, C; Viveiros, L de; Fertl, M; Formaggio, J A; Gladstone, L; Guigue, M; Heeger, K M; Johnston, J; Jones, A M; Kazkaz, K; LaRoque, B H; Lindman, A; Machado, E; Monreal, B; Morrison, E C; Nikkel, J A; ... mehr

The cyclotron radiation emission spectroscopy (CRES) technique pioneered by Project 8measures electromagnetic radiation fromindividual electrons gyrating in a backgroundmagnetic field to construct a highly precise energy spectrumfor beta decay studies and other applications. The detector,magnetic trap geometry and electron dynamics give rise to amultitude of complex electron signal structures which carry information about distinguishing physical traits.Withmachine learningmodels, we develop a scheme based on these traits to analyze and classifyCRES signals. Proper understanding and use of these traits will be instrumental to improve cyclotron frequency reconstruction and boost the potential of Project 8 to achieveworld-leading sensitivity on the tritiumendpointmeasurement in the future.

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Verlagsausgabe §
DOI: 10.5445/IR/1000117942
Veröffentlicht am 30.03.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Kernphysik (IKP)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 1367-2630
KITopen-ID: 1000117942
HGF-Programm 51.03.01 (POF III, LK 01) Neutrinophysik
Erschienen in New journal of physics
Verlag IOP Publishing
Band 22
Heft 3
Seiten Art. Nr.: 033004
Vorab online veröffentlicht am 03.03.2020
Nachgewiesen in Web of Science
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