Logo image
Machine Learning Techniques for Pile-Up Rejection in Cryogenic Calorimeters
Journal article   Open access   Peer reviewed

Machine Learning Techniques for Pile-Up Rejection in Cryogenic Calorimeters

G. Fantini, A. Armatol, E. Armengaud, W. Armstrong, C. Augier, F. T. Avignone, O. Azzolini, A. Barabash, G. Bari, A. Barresi, …
Journal of low temperature physics
27 May 2022
url
https://doi.org/10.1007/s10909-022-02741-9View
Published, Version of Record (VoR)CC BY V4.0 Open

Abstract

Physical Sciences Physics Physics, Applied Physics, Condensed Matter Science & Technology
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 Mo-100. 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.

Metrics

9 Record Views
4 citations in Scopus

Details

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Physics, Applied
Physics, Condensed Matter
Logo image