Machine Learning Techniques for Pile-Up Rejection in Cryogenic Calorimeters
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10909_2022_Article_2741.pdf
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1 MB
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Checksum (MD5)
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Author(s)
Fantini, G.
Armatol, A.
Armengaud, E.
Armstrong, W.
Augier, C.
Avignone, F. T.
Azzolini, O.
Barabash, A.
Bari, G.
Barresi, A.
Date Issued
May 27, 2022
Publisher
Springer US
Citation
Fantini, G., Armatol, A., Armengaud, E., Armstrong, W., Augier, C. et al. 2022. "Machine Learning Techniques for Pile-Up Rejection in Cryogenic Calorimeters."
Version
Final published version
Abstract
Abstract
CUORE Upgrade with Particle IDentification (CUPID) is a foreseen ton-scale array of Li2MoO4 (LMO) cryogenic calorimeters with double readout of heat and light signals. Its scientific goal is to fully explore the inverted hierarchy of neutrino masses in the search for neutrinoless double beta decay of 100Mo. Pile-up of standard double beta decay of the candidate isotope is a relevant background. We generate pile-up heat events via injection of Joule heater pulses with a programmable waveform generator in a small array of LMO crystals operated underground in the Laboratori Nazionali del Gran Sasso, Italy. This allows to label pile-up pulses and control both time difference and underlying amplitudes of individual heat pulses in the data. We present the performance of supervised learning classifiers on data and the attained pile-up rejection efficiency.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10909-022-02741-9
