Machine Learning Techniques for Pile-Up Rejection in Cryogenic Calorimeters

Author(s)

G. Fantini
A. Armatol
E. Armengaud
W. Armstrong
Frank Avignone III, University of South CarolinaFollow
O. Azzolini
A. Barabash
G. Bari
D. Baudin
F. Bellini
G. Benato
M. Beretta
L. Bergé
M. Biassoni
J. Billard
V. Boldrini
A. Branca
C. Brofferio
C. Bucci
J. Camilleri
Carl Rosenfeld, University of South CarolinaFollow
Carl Rosenfeld, University of South CarolinaFollow
Jeffery R. Wilson, Univrsity of South CarolinaFollow
Et. Al.

Document Type

Article

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.

Digital Object Identifier (DOI)

https://doi.org/10.1007/s10909-022-02741-9

Publication Info

Published in Journal of Low Temperature Physics, Volume 209, Issue 5-6, 2022, pages 1024-1031.

© The Author(s) 2022, corrected publication 2022 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

APA Citation

Fantini, G., A. Armatol, E. Armengaud, Armstrong, W., C. Augier, Avignone, F. T., Azzolini, O., Barabash, A., Bari, G., Barresi, A., Baudin, D., Bellini, F., G. Benato, Beretta, M., L. Bergé, M. Biassoni, Billard, J., V. Boldrini, Branca, A., & C. Brofferio. (2022). Machine Learning Techniques for Pile-Up Rejection in Cryogenic Calorimeters. Journal of Low Temperature Physics, 209(5-6), 1024–1031.https://doi.org/10.1007/s10909-022-02741-9