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.

Fantini, G., Armatol, A., Armengaud, E., Armstrong, W., Augier, C., Avignone, F., et al. (2022). Machine Learning Techniques for Pile-Up Rejection in Cryogenic Calorimeters. JOURNAL OF LOW TEMPERATURE PHYSICS [10.1007/s10909-022-02741-9].

Machine Learning Techniques for Pile-Up Rejection in Cryogenic Calorimeters

Barresi, A.;Biassoni, M.;Branca, A.;Brofferio, C.;Capelli, S.;Carniti, P.;Chiesa, D.;Clemenza, M.;Cova, F.;Cremonesi, O.;Fasoli, M.;Faverzani, M.;Giachero, A.;Gironi, L.;Gorla, P.;Gras, P.;Nastasi, M.;Nones, C.;Nutini, I.;Pagnanini, L.;Pessina, G.;Sisti, M.;Vedda, A.;
2022

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.
Articolo in rivista - Articolo scientifico
Scientifica
Convolutional neural networks; Cryogenic calorimeters; CUPID; Machine learning; Majorana; Neutrinoless double beta decay; Pile-up;
English
Fantini, G., Armatol, A., Armengaud, E., Armstrong, W., Augier, C., Avignone, F., et al. (2022). Machine Learning Techniques for Pile-Up Rejection in Cryogenic Calorimeters. JOURNAL OF LOW TEMPERATURE PHYSICS [10.1007/s10909-022-02741-9].
Fantini, G; Armatol, A; Armengaud, E; Armstrong, W; Augier, C; Avignone, F; 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; Cruciani, A; D’Addabbo, A; D’Imperio, G; Dafinei, I; Danevich, F; de Combarieu, M; De Jesus, M; de Marcillac, P; Dell’Oro, S; Domizio, S; 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; Gascon, J; Giachero, A; Gironi, L; Giuliani, A; Gorla, P; Gotti, C; Gras, P; Gros, M; Gutierrez, T; Han, K; Hansen, E; Heeger, K; Helis, D; Huang, H; Huang, R; Imbert, L; Johnston, J; Juillard, A; Karapetrov, G; Keppel, G; Khalife, H; Kobychev, V; Kolomensky, Y; Konovalov, S; Liu, Y; Loaiza, P; Ma, L; Madhukuttan, M; Mancarella, F; Mariam, R; Marini, L; Marnieros, S; Martinez, M; Maruyama, R; Mauri, B; Mayer, D; Mei, Y; Milana, S; Misiak, D; Napolitano, T; Nastasi, M; Navick, X; Nikkel, J; Nipoti, R; Nisi, S; Nones, C; Norman, E; Novosad, V; Nutini, I; O’Donnell, T; Olivieri, E; Oriol, C; Ouellet, J; 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; Polakovic, T; Polischuk, O; 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; Taffarello, L; Tellier, O; Tomei, C; Tretyak, V; Tsymbaliuk, A; Vedda, A; Velazquez, M; Vetter, K; Wagaarachchi, S; Wang, G; Wang, L; Welliver, B; Wilson, J; Wilson, K; Winslow, L; Xue, M; Yan, L; Yang, J; Yefremenko, V; Yumatov, V; Zarytskyy, M; Zhang, J; Zolotarova, A; Zucchelli, S
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/383890
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