Machine Learning Techniques for Pile-Up Rejection in Cryogenic Calorimeters

M Pavan ,Biswajit Paul ,K Vetter ,J R Wilson ,G Bari ,P Pari ,S Fu ,K Han ,E Celi ,J L Ouellet ,M Sisti ,G Benato ,M Beretta ,C Bucci ,A Zolotarova ,Giulia D'imperio ,С И Коновалов ,C Rusconi ,Changbo Fu ,V Dompè ,D Misiak ,S Pirro ,Tomas Polakovic ,F Ferri ,O G Polischuk ,I Colantoni ,E Ferri ,C Brofferio ,V I Tretyak ,S L Wagaarachchi ,R Maruyama ,F T Avignone ,M Madhukuttan ,O Cremonesi ,I Nutini ,A Giuliani ,G Wang ,A Franceschi ,S Pagan ,L Wang ,M Xue ,V Yumatov ,E Previtali ,P T Surukuchi ,A Armatol ,F Ferroni ,P De Marcillac ,Y Liu ,J Yang ,C Rosenfeld ,D Speller ,E Armengaud ,L A Winslow ,A D’addabbo ,V Singh ,J A Formaggio ,S Di Domizio ,D Mayer ,Silvia Milana ,G Karapetrov ,A Tsymbaliuk ,H Khalife ,V Novosad ,A Cruciani ,D Baudin ,L Cappelli ,Roberta Nipoti ,C Oriol ,E Figueroa‐Feliciano ,E B Norman ,P Loaiza ,K M Heeger ,O Azzolini ,S Pozzi ,L Imbert ,F Bellini ,R Rizzoli ,L Bergé ,K Wilson ,V Pettinacci ,M Martínez ,C Augier ,J Johnston ,T O’donnell ,Francesco Collamati ,P Carniti ,C Pagliarone ,A S Barabash ,S Marnieros ,V Kobychev ,E Olivieri ,A Juillard ,P Gorla ,H Z Huang ,D.v Poda ,J Zhang ,R J Creswick ,C Pira ,F.a Danevich ,Francesca Cova ,C Nones ,S Capelli ,D Chiesa ,M Biassoni ,J Billard ,Olivier Tellier ,F Mancarella ,T Napolitano ,L Pattavina ,V Sharma ,C Tomei ,L Marini ,Yu G Kolomensky ,M Gros ,M Nastasi ,S Nisi ,Andrea Barresi ,G Fantini ,E V Hansen ,M M Zarytskyy ,S Zucchelli ,G Pessina ,A Giachero ,L Pagnanini ,A Puiu ,X.f Navick ,V Sanglard ,M Clemenza ,A Cazes ,G Keppel ,W Armstrong ,M Velázquez ,M De Jésus ,Anne Charrier ,M Fasoli ,R Mariam ,M De Combarieu ,S Dell’oro ,A Drobizhev ,D Helis ,J Nikkel ,L Dumoulin ,S Copello ,L Gironi ,Y Mei ,L Ma ,V Boldrini ,H Peng ,I Dafinei ,A Vedda ,M Chapellier ,N Casali ,M Faverzani ,B Mauri ,B K Fujikawa ,V.n Shlegel ,T D Gutierrez ,B Welliver ,C L Chang ,J A Scarpaci ,Pierre Gras ,Benjamin Schmidt ,C Gotti ,A Branca ,A Ressa ,L Cardani ,V Yefremenko ,L Taffarello ,R G Huang ,J Gascon ,Yan Liang ,J Camilleri

doi:10.1007/s10909-022-02741-9

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.

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