Cyclotron Radiation Emission Spectroscopy Signal Classification with Machine Learning in Project 8

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, magnet...

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Bibliographic Details
Published in:arXiv.org 2020-03
Main Authors: A Ashtari Esfahani, Boser, S, Buzinsky, N, Cervantes, R, Claessens, C, de Viveiros, L, Fertl, M, maggio, 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, Novitski, E, Oblath, N S, Pettus, W, Robertson, R G H, Rybka, G, Saldana, L, Sibille, V, Schram, M, Slocum, P L, Sun, Y H, Thummler, T, VanDevender, B A, Weiss, T E, Wendler, T, Zayas, E
Format: Article
Language:English
Subjects:
Artificial intelligence
Background radiation
Beta decay
Cyclotron frequency
Cyclotron radiation
Cyclotrons
Electromagnetic radiation
Emission spectroscopy
Energy spectra
Machine learning
Signal classification
Spectrum analysis
Tritium
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Summary: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. Understanding and proper use of these traits will be instrumental to improve cyclotron frequency reconstruction and help Project 8 achieve world-leading sensitivity on the tritium endpoint measurement in the future.
ISSN:2331-8422
DOI:10.48550/arxiv.1909.08115