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. Brofferio; C. 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

doi:10.1007/s10909-022-02741-9

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

Physics and Astronomy

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

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.

Original language	English (US)
Pages (from-to)	1024-1031
Number of pages	8
Journal	Journal of Low Temperature Physics
Volume	209
Issue number	5-6
DOIs	https://doi.org/10.1007/s10909-022-02741-9
State	Published - Dec 2022

Keywords

CUPID
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

Access to Document

10.1007/s10909-022-02741-9

Cite this

Fantini, 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., ... Zucchelli, S. (2022). Machine Learning Techniques for Pile-Up Rejection in Cryogenic Calorimeters. Journal of Low Temperature Physics, 209(5-6), 1024-1031. https://doi.org/10.1007/s10909-022-02741-9

@article{63795773b9ce4a0eb63cd640931c42df,

title = "Machine Learning Techniques for Pile-Up Rejection in Cryogenic Calorimeters",

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.",

keywords = "CUPID, Convolutional neural networks, Cryogenic calorimeters, Machine learning, Majorana, Neutrinoless double beta decay, Pile-up",

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

note = "Funding Information: The CUPID Collaboration thanks the directors and staff of the Laboratori Nazionali del Gran Sasso and the technical staff of our laboratories. This work was supported by the Istituto Nazionale di Fisica Nucleare (INFN); by the European Research Council (ERC) under the European Union Horizon 2020 program (H2020/2014-2020) with the ERC Advanced Grant No. 742345 (ERC-2016-ADG, project CROSS) and the Marie Sklodowska-Curie Grant Agreement No. 754496; by the Italian Ministry of University and Research (MIUR) through the grant Progetti di ricerca di Rilevante Interesse Nazionale (PRIN 2017, Grant No. 2017FJZMCJ); by the US National Science Foundation under Grants No. NSF-PHY-1401832, No. NSF-PHY-1614611 and No. NSF-PHY-1913374. This material is also based upon work supported by the US Department of Energy (DOE) Office of Science under Contracts No. DE-AC02- 05CH11231 and NO. DE-AC02-06CH11357; and by the DOE Office of Science, Office of Nuclear Physics under Contracts No. DE-FG02-08ER41551, No. DE-SC0011091, No. DESC0012654, No. DE-SC0019316, No. DE-SC0019368, and No. DE-SC0020423. This work was also supported by the Russian Science Foundation under Grant No. 18-12-00003 and the National Research Foundation of Ukraine under Grant No. 2020.02/0011. This research used resources of the National Energy Research Scientific Computing Center (NERSC). This work makes use of both the DIANA data analysis and APOLLO data acquisition software packages, which were developed by the CUORICINO, CUORE, LUCIFER and CUPID-0 Collaborations. Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

month = dec,

doi = "10.1007/s10909-022-02741-9",

language = "English (US)",

volume = "209",

pages = "1024--1031",

journal = "Journal of Low Temperature Physics",

issn = "0022-2291",

publisher = "Springer New York",

number = "5-6",

}

Fantini, G, Armatol, A, Armengaud, E, Armstrong, W, Augier, C, Avignone, FT, 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, RJ, Cruciani, A, D’Addabbo, A, D’Imperio, G, Dafinei, I, Danevich, FA, de Combarieu, M, De Jesus, M, de Marcillac, P, Dell’Oro, S, Domizio, SD, 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, BK, Gascon, J, Giachero, A, Gironi, L, Giuliani, A, Gorla, P, Gotti, C, Gras, P, Gros, M, Gutierrez, TD, Han, K, Hansen, EV, Heeger, KM, Helis, DL, Huang, HZ, Huang, RG, Imbert, L, Johnston, J, Juillard, A, Karapetrov, G, Keppel, G, Khalife, H, Kobychev, VV, Kolomensky, YG, Konovalov, S, Liu, Y, Loaiza, P, Ma, L, Madhukuttan, M, Mancarella, F, Mariam, R, Marini, L, Marnieros, S, Martinez, M, Maruyama, RH, Mauri, B, Mayer, D, Mei, Y, Milana, S, Misiak, D, Napolitano, T, Nastasi, M, Navick, XF, Nikkel, J, Nipoti, R, Nisi, S, Nones, C, Norman, EB, Novosad, V, Nutini, I, O’Donnell, T, Olivieri, E, Oriol, C, Ouellet, JL, 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, DV, Polakovic, T, Polischuk, OG, 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, PT, Taffarello, L, Tellier, O, Tomei, C, Tretyak, VI, Tsymbaliuk, A, Vedda, A, Velazquez, M, Vetter, KJ, Wagaarachchi, SL, Wang, G, Wang, L, Welliver, B, Wilson, J, Wilson, K, Winslow, LA, Xue, M, Yan, L, Yang, J, Yefremenko, V, Yumatov, V, Zarytskyy, MM, Zhang, J, Zolotarova, A & Zucchelli, S 2022, 'Machine Learning Techniques for Pile-Up Rejection in Cryogenic Calorimeters', Journal of Low Temperature Physics, vol. 209, no. 5-6, pp. 1024-1031. https://doi.org/10.1007/s10909-022-02741-9

TY - JOUR

T1 - Machine Learning Techniques for Pile-Up Rejection in Cryogenic Calorimeters

AU - Fantini, G.

AU - Armatol, A.

AU - Armengaud, E.

AU - Armstrong, W.

AU - Augier, C.

AU - Avignone, F. T.

AU - Azzolini, O.

AU - Barabash, A.

AU - Bari, G.

AU - Barresi, A.

AU - Baudin, D.

AU - Bellini, F.

AU - Benato, G.

AU - Beretta, M.

AU - Bergé, L.

AU - Biassoni, M.

AU - Billard, J.

AU - Boldrini, V.

AU - Branca, A.

AU - Brofferio, C.

AU - Bucci, C.

AU - Camilleri, J.

AU - Capelli, S.

AU - Cappelli, L.

AU - Cardani, L.

AU - Carniti, P.

AU - Casali, N.

AU - Cazes, A.

AU - Celi, E.

AU - Chang, C.

AU - Chapellier, M.

AU - Charrier, A.

AU - Chiesa, D.

AU - Clemenza, M.

AU - Colantoni, I.

AU - Collamati, F.

AU - Copello, S.

AU - Cova, F.

AU - Cremonesi, O.

AU - Creswick, R. J.

AU - Cruciani, A.

AU - D’Addabbo, A.

AU - D’Imperio, G.

AU - Dafinei, I.

AU - Danevich, F. A.

AU - de Combarieu, M.

AU - De Jesus, M.

AU - de Marcillac, P.

AU - Dell’Oro, S.

AU - Domizio, S. Di

AU - Dompè, V.

AU - Drobizhev, A.

AU - Dumoulin, L.

AU - Fasoli, M.

AU - Faverzani, M.

AU - Ferri, E.

AU - Ferri, F.

AU - Ferroni, F.

AU - Figueroa-Feliciano, E.

AU - Formaggio, J.

AU - Franceschi, A.

AU - Fu, C.

AU - Fu, S.

AU - Fujikawa, B. K.

AU - Gascon, J.

AU - Giachero, A.

AU - Gironi, L.

AU - Giuliani, A.

AU - Gorla, P.

AU - Gotti, C.

AU - Gras, P.

AU - Gros, M.

AU - Gutierrez, T. D.

AU - Han, K.

AU - Hansen, E. V.

AU - Heeger, K. M.

AU - Helis, D. L.

AU - Huang, H. Z.

AU - Huang, R. G.

AU - Imbert, L.

AU - Johnston, J.

AU - Juillard, A.

AU - Karapetrov, G.

AU - Keppel, G.

AU - Khalife, H.

AU - Kobychev, V. V.

AU - Kolomensky, Yu G.

AU - Konovalov, S.

AU - Liu, Y.

AU - Loaiza, P.

AU - Ma, L.

AU - Madhukuttan, M.

AU - Mancarella, F.

AU - Mariam, R.

AU - Marini, L.

AU - Marnieros, S.

AU - Martinez, M.

AU - Maruyama, R. H.

AU - Mauri, B.

AU - Mayer, D.

AU - Mei, Y.

AU - Milana, S.

AU - Misiak, D.

AU - Napolitano, T.

AU - Nastasi, M.

AU - Navick, X. F.

AU - Nikkel, J.

AU - Nipoti, R.

AU - Nisi, S.

AU - Nones, C.

AU - Norman, E. B.

AU - Novosad, V.

AU - Nutini, I.

AU - O’Donnell, T.

AU - Olivieri, E.

AU - Oriol, C.

AU - Ouellet, J. L.

AU - Pagan, S.

AU - Pagliarone, C.

AU - Pagnanini, L.

AU - Pari, P.

AU - Pattavina, L.

AU - Paul, B.

AU - Pavan, M.

AU - Peng, H.

AU - Pessina, G.

AU - Pettinacci, V.

AU - Pira, C.

AU - Pirro, S.

AU - Poda, D. V.

AU - Polakovic, T.

AU - Polischuk, O. G.

AU - Pozzi, S.

AU - Previtali, E.

AU - Puiu, A.

AU - Ressa, A.

AU - Rizzoli, R.

AU - Rosenfeld, C.

AU - Rusconi, C.

AU - Sanglard, V.

AU - Scarpaci, J.

AU - Schmidt, B.

AU - Sharma, V.

AU - Shlegel, V.

AU - Singh, V.

AU - Sisti, M.

AU - Speller, D.

AU - Surukuchi, P. T.

AU - Taffarello, L.

AU - Tellier, O.

AU - Tomei, C.

AU - Tretyak, V. I.

AU - Tsymbaliuk, A.

AU - Vedda, A.

AU - Velazquez, M.

AU - Vetter, K. J.

AU - Wagaarachchi, S. L.

AU - Wang, G.

AU - Wang, L.

AU - Welliver, B.

AU - Wilson, J.

AU - Wilson, K.

AU - Winslow, L. A.

AU - Xue, M.

AU - Yan, L.

AU - Yang, J.

AU - Yefremenko, V.

AU - Yumatov, V.

AU - Zarytskyy, M. M.

AU - Zhang, J.

AU - Zolotarova, A.

AU - Zucchelli, S.

N1 - Funding Information: The CUPID Collaboration thanks the directors and staff of the Laboratori Nazionali del Gran Sasso and the technical staff of our laboratories. This work was supported by the Istituto Nazionale di Fisica Nucleare (INFN); by the European Research Council (ERC) under the European Union Horizon 2020 program (H2020/2014-2020) with the ERC Advanced Grant No. 742345 (ERC-2016-ADG, project CROSS) and the Marie Sklodowska-Curie Grant Agreement No. 754496; by the Italian Ministry of University and Research (MIUR) through the grant Progetti di ricerca di Rilevante Interesse Nazionale (PRIN 2017, Grant No. 2017FJZMCJ); by the US National Science Foundation under Grants No. NSF-PHY-1401832, No. NSF-PHY-1614611 and No. NSF-PHY-1913374. This material is also based upon work supported by the US Department of Energy (DOE) Office of Science under Contracts No. DE-AC02- 05CH11231 and NO. DE-AC02-06CH11357; and by the DOE Office of Science, Office of Nuclear Physics under Contracts No. DE-FG02-08ER41551, No. DE-SC0011091, No. DESC0012654, No. DE-SC0019316, No. DE-SC0019368, and No. DE-SC0020423. This work was also supported by the Russian Science Foundation under Grant No. 18-12-00003 and the National Research Foundation of Ukraine under Grant No. 2020.02/0011. This research used resources of the National Energy Research Scientific Computing Center (NERSC). This work makes use of both the DIANA data analysis and APOLLO data acquisition software packages, which were developed by the CUORICINO, CUORE, LUCIFER and CUPID-0 Collaborations. Publisher Copyright: © 2022, The Author(s).

PY - 2022/12

Y1 - 2022/12

N2 - 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.

AB - 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.

KW - CUPID

KW - Convolutional neural networks

KW - Cryogenic calorimeters

KW - Machine learning

KW - Majorana

KW - Neutrinoless double beta decay

KW - Pile-up

UR - http://www.scopus.com/inward/record.url?scp=85130734348&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85130734348&partnerID=8YFLogxK

U2 - 10.1007/s10909-022-02741-9

DO - 10.1007/s10909-022-02741-9

M3 - Article

AN - SCOPUS:85130734348

SN - 0022-2291

VL - 209

SP - 1024

EP - 1031

JO - Journal of Low Temperature Physics

JF - Journal of Low Temperature Physics

IS - 5-6

ER -

Machine Learning Techniques for Pile-Up Rejection in Cryogenic Calorimeters

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