Journal ArticleMachine Learning Techniques for Pile-Up Rejection in Cryogenic CalorimetersFantini, G.; Armatol, A.; Armengaud, E.; Armstrong, W.; Augier, C.; Avignone, F. T.; Azzolini, O.; Barabash, A.; Bari, G.; Barresi, A.; Baudin, D.; Bellini, F.; Benato, G.; Beretta, M.; Bergé, L.; Biassoni, M.; Billard, J.; Boldrini, V.; Branca, A.; Brofferio, C.; Bucci, C.; Camilleri, J.; Capelli, S.; Cappelli, L.; Cardani, L.; Carniti, P.; Casali, N.; Cazes, A.; Celi, E.; Chang, C.; Chapellier, M.; Charrier, A.; Chiesa, D.; Clemenza, M.; Colantoni, I.; Collamati, F.; Copello, S.; Cova, F.; Cremonesi, O.; Creswick, R. J.; Cruciani, A.; D’Addabbo, A.; D’Imperio, G.; Dafinei, I.; Danevich, F. A.; de Combarieu, M.; De Jesus, M.; de Marcillac, P.; Dell’Oro, S.; Domizio, S. Di; Dompè, V.; Drobizhev, A.; Dumoulin, L.; Fasoli, M.; Faverzani, M.; Ferri, E.; Ferri, F.; Ferroni, F.; Figueroa-Feliciano, E.; Formaggio, J.; Franceschi, A.; Fu, C.; Fu, S.; Fujikawa, B. K.; Gascon, J.; Giachero, A.; Gironi, L.; Giuliani, A.; Gorla, P.; Gotti, C.; Gras, P.; Gros, M.; Gutierrez, T. D.; Han, K.; Hansen, E. V.; Heeger, K. M.; Helis, D. L.; Huang, H. Z.; Huang, R. G.; Imbert, L.; Johnston, J.; Juillard, A.; Karapetrov, G.; Keppel, G.; Khalife, H.; Kobychev, V. V.; Kolomensky, Yu. G.; Konovalov, S.; Liu, Y.; Loaiza, P.; Ma, L.; Madhukuttan, M.; Mancarella, F.; Mariam, R.; Marini, L.; Marnieros, S.; Martinez, M.; Maruyama, R. H.; Mauri, B.; Mayer, D.; Mei, Y.; Milana, S.; Misiak, D.; Napolitano, T.; Nastasi, M.; Navick, X. F.; Nikkel, J.; Nipoti, R.; Nisi, S.; Nones, C.; Norman, E. B.; Novosad, V.; Nutini, I.; O’Donnell, T.; Olivieri, E.; Oriol, C.; Ouellet, J. L.; Pagan, S.; Pagliarone, C.; Pagnanini, L.; Pari, P.; Pattavina, L.; Paul, B.; Pavan, M.; Peng, H.; Pessina, G.; Pettinacci, V.; Pira, C.; Pirro, S.; Poda, D. V.; Polakovic, T.; Polischuk, O. G.; Pozzi, S.; Previtali, E.; Puiu, A.; Ressa, A.; Rizzoli, R.; Rosenfeld, C.; Rusconi, C.; Sanglard, V.; Scarpaci, J.; Schmidt, B.; Sharma, V.; Shlegel, V.; Singh, V.; Sisti, M.; Speller, D.; Surukuchi, P. T.; Taffarello, L.; Tellier, O.; Tomei, C.; Tretyak, V. I.; Tsymbaliuk, A.; Vedda, A.; Velazquez, M.; Vetter, K. J.; Wagaarachchi, S. L.; Wang, G.; Wang, L.; Welliver, B.; Wilson, J.; Wilson, K.; Winslow, L. A.; Xue, M.; Yan, L.; Yang, J.; Yefremenko, V.; Yumatov, V.; Zarytskyy, M. M.; Zhang, J.; Zolotarova, A.; Zucchelli, S.Abstract CUORE Upgrade with Particle IDentification (CUPID) is a foreseen ton-scale array of Li 2 MoO 4 (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 100 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.2022-12-0110427833Journal of Low Temperature Physics2095-61024 to 10310022-2291https://doi.org/10.1007/s10909-022-02741-92209585National Science Foundation