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1826211677349085184
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MIT
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| description |
© 2020 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft. 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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2024-09-23T15:09:46Z
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Article
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mit-1721.1/132432
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| institution |
Massachusetts Institute of Technology
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| language |
English
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2024-09-23T15:09:46Z
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| publishDate |
2021
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| publisher |
IOP Publishing
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dspace
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| spelling |
mit-1721.1/1324322022-04-01T17:27:47Z Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8 © 2020 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft. 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. 2021-09-20T18:22:21Z 2021-09-20T18:22:21Z 2020-10-21T18:07:08Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/132432 en 10.1088/1367-2630/AB71BD New Journal of Physics Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf IOP Publishing IOP Publishing
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| spellingShingle |
Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8
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| title |
Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8
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| title_full |
Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8
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| title_fullStr |
Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8
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| title_full_unstemmed |
Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8
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| title_short |
Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8
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| title_sort |
cyclotron radiation emission spectroscopy signal classification with machine learning in project 8
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| url |
https://hdl.handle.net/1721.1/132432
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