2026-09-18T23:17:57Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1428242026-08-04T04:29:50Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Formaggio, Joseph A.
Buzinsky, Nicholas
2022-05-31T13:30:43Z
2022-05-31T13:30:43Z
2021-09
2022-05-25T19:52:54.184Z
https://hdl.handle.net/1721.1/142824
Despite the unambiguous discovery of non-zero neutrino masses from flavor oscillation experiments, a direct measurement of the absolute mass scale of the neutrino remains elusive to experimentalists. Project 8 is a tritium endpoint experiment utilizing Cyclotron Radiation Emission Spectroscopy (CRES), a novel, high-precision spectroscopic technique, in order to establish the absolute neutrino mass scale. In this document, I investigate the statistically motivated limits to CRES signal detection and parameter estimation, as well as the resultant consequences on optimal detector configuration. I implement and test an application of the Viterbi algorithm for CRES signal reconstruction, yielding the first derived limits on the minimal detection criteria. I then present an original derivation of the Cramér-Rao Lower Bound of the start frequency resolution for realistic CRES signals, along with estimators yielding near-optimal performance. Finally, these improved detection and reconstruction algorithms lead into a discussion of optimal detector design.
Ph.D.
Massachusetts Institute of Technology
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http://rightsstatements.org/page/InC-EDU/1.0/
Statistical Signal Processing and Detector Optimization in Project 8
Thesis
Massachusetts Institute of Technology. Department of Physics
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Doctoral
Doctor of Philosophy
Publication
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<Abstract>Despite the unambiguous discovery of non-zero neutrino masses from flavor oscillation experiments, a direct measurement of the absolute mass scale of the neutrino remains elusive to experimentalists. Project 8 is a tritium endpoint experiment utilizing Cyclotron Radiation Emission Spectroscopy (CRES), a novel, high-precision spectroscopic technique, in order to establish the absolute neutrino mass scale. In this document, I investigate the statistically motivated limits to CRES signal detection and parameter estimation, as well as the resultant consequences on optimal detector configuration. I implement and test an application of the Viterbi algorithm for CRES signal reconstruction, yielding the first derived limits on the minimal detection criteria. I then present an original derivation of the Cramér-Rao Lower Bound of the start frequency resolution for realistic CRES signals, along with estimators yielding near-optimal performance. Finally, these improved detection and reconstruction algorithms lead into a discussion of optimal detector design.</Abstract>
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