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, D. Baudin, F. Bellini, G. Benato, M. Beretta, L. Bergé, M. Biassoni, J. Billard, V. Boldrini, A. Branca, C. BrofferioC. Bucci, J. Camilleri, S. Capelli, L. Cappelli, L. Cardani, P. Carniti, N. Casali, A. Cazes, E. Celi, C. Chang, M. Chapellier, A. Charrier, D. Chiesa, M. Clemenza, I. Colantoni, F. Collamati, S. Copello, F. Cova, O. Cremonesi, R. J. Creswick, A. Cruciani, A. D’Addabbo, G. D’Imperio, I. Dafinei, F. A. Danevich, M. de Combarieu, M. De Jesus, P. de Marcillac, S. Dell’Oro, S. Di Domizio, V. Dompè, A. Drobizhev, L. Dumoulin, M. Fasoli, M. Faverzani, E. Ferri, F. Ferri, F. Ferroni, E. Figueroa-Feliciano, J. Formaggio, A. Franceschi, C. Fu, S. Fu, B. K. Fujikawa, J. Gascon, A. Giachero, L. Gironi, A. Giuliani, P. Gorla, C. Gotti, P. Gras, M. Gros, T. D. Gutierrez, K. Han, E. V. Hansen, K. M. Heeger, D. L. Helis, H. Z. Huang, R. G. Huang, L. Imbert, J. Johnston, A. Juillard, G. Karapetrov, G. Keppel, H. Khalife, V. V. Kobychev, Yu G. Kolomensky, S. Konovalov, Y. Liu, P. Loaiza, L. Ma, M. Madhukuttan, F. Mancarella, R. Mariam, L. Marini, S. Marnieros, M. Martinez, R. H. Maruyama, B. Mauri, D. Mayer, Y. Mei, S. Milana, D. Misiak, T. Napolitano, M. Nastasi, X. F. Navick, J. Nikkel, R. Nipoti, S. Nisi, C. Nones, E. B. Norman, V. Novosad, I. Nutini, T. O’Donnell, E. Olivieri, C. Oriol, J. L. Ouellet, S. Pagan, C. Pagliarone, L. Pagnanini, P. Pari, L. Pattavina, B. Paul, M. Pavan, H. Peng, G. Pessina, V. Pettinacci, C. Pira, S. Pirro, D. V. Poda, T. Polakovic, O. G. Polischuk, S. Pozzi, E. Previtali, A. Puiu, A. Ressa, R. Rizzoli, C. Rosenfeld, C. Rusconi, V. Sanglard, J. Scarpaci, B. Schmidt, V. Sharma, V. Shlegel, V. Singh, M. Sisti, D. Speller, P. T. Surukuchi, L. Taffarello, O. Tellier, C. Tomei, V. I. Tretyak, A. Tsymbaliuk, A. Vedda, M. Velazquez, K. J. Vetter, S. L. Wagaarachchi, G. Wang, L. Wang, B. Welliver, J. Wilson, K. Wilson, L. A. Winslow, M. Xue, L. Yan, J. Yang, V. Yefremenko, V. Yumatov, M. M. Zarytskyy, J. Zhang, A. Zolotarova, S. Zucchelli

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1024-1031
Number of pages8
JournalJournal of Low Temperature Physics
Issue number5-6
StatePublished - Dec 2022


  • Convolutional neural networks
  • Cryogenic calorimeters
  • Machine learning
  • Majorana
  • Neutrinoless double beta decay
  • Pile-up

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Materials Science(all)
  • Condensed Matter Physics


Dive into the research topics of 'Machine Learning Techniques for Pile-Up Rejection in Cryogenic Calorimeters'. Together they form a unique fingerprint.

Cite this