Electromagnetic shower reconstruction and energy validation with Michel electrons and π0 samples for the deep-learning-based analyses in MicroBooNE. (22nd December 2021)

• Record Type: Journal Article
• Authors: Abratenko, P.; An, R.; Anthony, J.; Arellano, L.; Asaadi, J.; Ashkenazi, A.; Balasubramanian, S.; Baller, B.; Barnes, C.; Barr, G.; Basque, V.; Bathe-Peters, L.; Benevides Rodrigues, O.; Berkman, S.; Bhanderi, A.; Bhat, A.; Bishai, M.; Blake, A.; Bolton, T.; Book, J.Y.; Camilleri, L.; Caratelli, D.; Caro Terrazas, I.; Castillo Fernandez, R.; Cavanna, F.; Cerati, G.; Chen, Y.; Cianci, D.; Conrad, J.M.; Convery, M.; Cooper-Troendle, L.; Crespo-Anadón, J.I.; Del Tutto, M.; Dennis, S.R.; Detje, P.; Devitt, A.; Diurba, R.; Dorrill, R.; Duffy, K.; Dytman, S.; Eberly, B.; Ereditato, A.; Evans, J.J.; Fine, R.; Fiorentini Aguirre, G.A.; Fitzpatrick, R.S.; Fleming, B.T.; Foppiani, N.; Franco, D.; Furmanski, A.P.; Garcia-Gamez, D.; Gardiner, S.; Ge, G.; Gollapinni, S.; Goodwin, O.; Gramellini, E.; Green, P.; Greenlee, H.; Gu, W.; Guenette, R.; Guzowski, P.; Hagaman, L.; Hen, O.; Hilgenberg, C.; Horton-Smith, G.A.; Hourlier, A.; Itay, R.; James, C.; Ji, X.; Jiang, L.; Jo, J.H.; Johnson, R.A.; Jwa, Y.-J.; Kalra, D.; Kamp, N.; Kaneshige, N.; Karagiorgi, G.; Ketchum, W.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; LaZur, R.; Lepetic, I.; Li, K.; Li, Y.; Lin, K.; Littlejohn, B.R.; Louis, W.C.; Luo, X.; Manivannan, K.; Mariani, C.; Marsden, D.; Marshall, J.; Martinez Caicedo, D.A.; Mason, K.; Mastbaum, A.; McConkey, N.; Meddage, V.; Mettler, T.; Miller, K.; Mills, J.; Mistry, K.; Mogan, A.; Mohayai, T.; Moon, J.; Mooney, M.; Moor, A.F.; Moore, C.D.; Mora Lepin, L.; Mousseau, J.; Murphy, M.; Naples, D.; Navrer-Agasson, A.; Nebot-Guinot, M.; Neely, R.K.; Newmark, D.A.; Nowak, J.; Nunes, M.; Palamara, O.; Paolone, V.; Papadopoulou, A.; Papavassiliou, V.; Pate, S.F.; Patel, N.; Paudel, A.; Pavlovic, Z.; Piasetzky, E.; Ponce-Pinto, I.D.; Prince, S.; Qian, X.; Raaf, J.L.; Radeka, V.; Rafique, A.; Reggiani-Guzzo, M.; Ren, L.; Rice, L.C.J.; Rochester, L.; Rodriguez Rondon, J.; Rosenberg, M.; Ross-Lonergan, M.; Scanavini, G.; Schmitz, D.W.; Schukraft, A.; Seligman, W.; Shaevitz, M.H.; Sharankova, R.; Shi, J.; Sinclair, J.; Smith, A.; Snider, E.L.; Soderberg, M.; Söldner-Rembold, S.; Spentzouris, P.; Spitz, J.; Stancari, M.; St., J.; Strauss, T.; Sutton, K.; Sword-Fehlberg, S.; Szelc, A.M.; Tagg, N.; Tang, W.; Terao, K.; Thorpe, C.; Totani, D.; Toups, M.; Tsai, Y.-T.; Uchida, M.A.; Usher, T.; Van De Pontseele, W.; Viren, B.; Weber, M.; Wei, H.; Williams, Z.; Wolbers, S.; Wongjirad, T.; Wospakrik, M.; Wresilo, K.; Wright, N.; Wu, W.; Yandel, E.; Yang, T.; Yarbrough, G.; Yates, L.E.; Yu, H.W.; Zeller, G.P.; Zennamo, J.; Zhang, C.;
• Other Names: collab.
• Is Part Of: Journal of instrumentation. Volume 16:Number 12(2021)
• Publication Date: 2021-12-22
• Subjects: Neutrino detectors -- Noble liquid detectors (scintillation, ionization, double-phase) -- Pattern recognition, cluster finding, calibration and fitting methods -- Time projection Chambers (TPC); Scientific apparatus and instruments -- Periodicals; 502.84
• DOI: 10.1088/1748-0221/16/12/T12017
• Languages: English
• ISSNs: 1748-0221

Abstract

Abstract: This article presents the reconstruction of the electromagnetic activity from electrons and photons (showers) used in the MicroBooNE deep learning-based low energy electron search. The reconstruction algorithm uses a combination of traditional and deep learning-based techniques to estimate shower energies. We validate these predictions using two νμ -sourced data samples: charged/neutral current interactions with final state neutral pions and charged current interactions in which the muon stops and decays within the detector producing a Michel electron. Both the neutral pion sample and Michel electron sample demonstrate agreement between data and simulation. Further, the absolute shower energy scale is shown to be consistent with the relevant physical constant of each sample: the neutral pion mass peak and the Michel energy cutoff.