CUORE Upgrade with Particle IDentification (CUPID) is a foreseen ton-scale array of Li_{2} 2 MoO_{4} 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} 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.
Machine learning techniques for pile-up rejection in cryogenic calorimeters / Fantini, G.; Armatol, A.; Armengaud, E.; Armstrong, W.; Augier, C.; Avignone, F. T.; Iii, ; 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.; Di 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. 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.. - In: JOURNAL OF LOW TEMPERATURE PHYSICS. - ISSN 0022-2291. - 209:5-6(2022), pp. 1024-1031. (Intervento presentato al convegno 19th international workshop on low temperature detectors (LTD19) tenutosi a Virtual) [10.1007/s10909-022-02741-9].
Machine learning techniques for pile-up rejection in cryogenic calorimeters
G. Fantini
Primo
Methodology
;F. Bellini;L. Cardani;N. Casali;E. Celi;I. Colantoni;F. Collamati;A. Cruciani;G. D’Imperio;I. Dafinei;V. Dompè;F. Ferroni;L. Marini;M. Martinez;S. Milana;V. Pettinacci;A. Ressa;
2022
Abstract
CUORE Upgrade with Particle IDentification (CUPID) is a foreseen ton-scale array of Li_{2} 2 MoO_{4} 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} 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.| File | Dimensione | Formato | |
|---|---|---|---|
|
Fantini_machine-learning-techniques_2022.pdf
accesso aperto
Note: Articolo su rivista
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Creative commons
Dimensione
1.02 MB
Formato
Adobe PDF
|
1.02 MB | Adobe PDF |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
