Skip to Main Content (Press Enter)

Logo CNR
  • ×
  • Home
  • People
  • Outputs
  • Organizations
  • Expertise & Skills

UNI-FIND
Logo CNR

|

UNI-FIND

cnr.it
  • ×
  • Home
  • People
  • Outputs
  • Organizations
  • Expertise & Skills
  1. Outputs

Machine Learning Techniques for Pile-Up Rejection in Cryogenic Calorimeters

Academic Article
Publication Date:
2022
abstract:
CUORE Upgrade with Particle IDentification (CUPID) is a foreseen ton-scale array of LiMoO (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. 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.
Iris type:
01.01 Articolo in rivista
Keywords:
Convolutional neural networks; Cryogenic calorimeters; CUPID; Machine learning; Majorana; Neutrinoless double beta decay; Pile-up
List of contributors:
Boldrini, Virginia; Nipoti, Roberta; Mancarella, Fulvio; Colantoni, Ivan; Rizzoli, Rita
Authors of the University:
COLANTONI IVAN
MANCARELLA FULVIO
RIZZOLI RITA
Handle:
https://iris.cnr.it/handle/20.500.14243/413567
Published in:
JOURNAL OF LOW TEMPERATURE PHYSICS
Journal
  • Overview

Overview

URL

http://www.scopus.com/record/display.url?eid=2-s2.0-85130734348&origin=inward
  • Use of cookies

Powered by VIVO | Designed by Cineca | 26.5.0.0 | Sorgente dati: PREPROD (Ribaltamento disabilitato)