Journal
NEW JOURNAL OF PHYSICS
Volume 22, Issue 3, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/1367-2630/ab71bd
Keywords
neutrino mass; cyclotron radiation; machine learning; support vector machine
Categories
Funding
- US Department of Energy Office of Science, Office of Nuclear Physics [DE-SC0011091]
- US Department of Energy [DE-AC05-76RL01830, DE-SC0019088, DE-FG02-97ER41020, DE-SC0012654]
- National Science Foundation [1205100, 1505678]
- Cluster of Excellence 'Precision Physics, Fundamental Interactions, and Structure of Matter' (PRISMA) - German Research Foundation (DFG) [EXC 2118/1, 39083149]
- Laboratory Directed Research and Development (LDRD) at Lawrence Livermore National Laboratory (LLNL) [18-ERD-028, DE-AC52-07NA27344]
- MIT Wade Fellowship
- LDRD Program at PNNL
- University of Washington Royalty Research Foundation
- Yale University
- PRISMA Cluster of Excellence at the University of Mainz
- Karlsruhe Institute of Technology (KIT) Center Elementary Particle and Astroparticle Physics (KCETA)
- U.S. Department of Energy (DOE) [DE-SC0012654, DE-SC0019088] Funding Source: U.S. Department of Energy (DOE)
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The cyclotron radiation emission spectroscopy (CRES) technique pioneered by Project 8 measures electromagnetic radiation from individual electrons gyrating in a background magnetic field to construct a highly precise energy spectrum for beta decay studies and other applications. The detector, magnetic trap geometry and electron dynamics give rise to a multitude of complex electron signal structures which carry information about distinguishing physical traits. With machine learning models, we develop a scheme based on these traits to analyze and classify CRES signals. Proper understanding and use of these traits will be instrumental to improve cyclotron frequency reconstruction and boost the potential of Project 8 to achieve world-leading sensitivity on the tritium endpoint measurement in the future.
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