Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8
Name
Esfahani_2020_New_J._Phys._22_033004.pdf
Description
Published version
Size
2.15 MB
Format
Unknown
Checksum (MD5)
672ecdfe4863ec19d046599544b11b13
Author(s)
Buzinsky, Nicholas Gregory
Formaggio, Joseph A
Johnston, Joe
Sibille, Valerian
Weiss, Talia E.
Zayas, Evan M.
Date Issued
2020
Journal
New Journal of Physics
Publisher
IOP Publishing
Version
Final published version
Abstract
© 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.
MIT Department
Massachusetts Institute of Technology. Laboratory for Nuclear Science
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1088/1367-2630/AB71BD
