Electromagnetic shower reconstruction and energy validation with Michel electrons and p0samples for the deep-learning-based analyses in MicroBooNE

P. Abratenko; R. An; J. Anthony; L. Arellano; J. Asaadi; A. Ashkenazi; S. Balasubramanian; B. Baller; C. Barnes; G. Barr; V. Basque; L. Bathe-Peters; O. Benevides Rodrigues; S. Berkman; A. Bhanderi; A. Bhat; M. Bishai; A. Blake; T. Bolton; J. Y. Book; L. Camilleri; D. Caratelli; I. Caro Terrazas; R. Castillo Fernandez; F. Cavanna; G. Cerati; Y. Chen; D. Cianci; J. M. Conrad; M. Convery; L. Cooper-Troendle; J. I. Crespo-Anadón; M. Del Tutto; S. R. Dennis; P. Detje; A. Devitt; R. Diurba; R. Dorrill; K. Duffy; S. Dytman; B. Eberly; A. Ereditato; J. J. Evans; R. Fine; G. A. Fiorentini Aguirre; R. S. Fitzpatrick; B. T. Fleming; N. Foppiani; D. Franco; A. P. Furmanski; D. Garcia-Gamez; S. Gardiner; G. Ge; S. Gollapinni; O. Goodwin; E. Gramellini; P. Green; H. Greenlee; W. Gu; R. Guenette; P. Guzowski; L. Hagaman; O. Hen; C. Hilgenberg; G. A. Horton-Smith; A. Hourlier; R. Itay; C. James; X. Ji; L. Jiang; J. H. Jo; R. A. Johnson; Y. J. Jwa; D. Kalra; N. Kamp; N. Kaneshige; G. Karagiorgi; W. Ketchum; M. Kirby; T. Kobilarcik; I. Kreslo; R. Lazur; I. Lepetic; K. Li; Y. Li; K. Lin; B. R. Littlejohn; W. C. Louis; X. Luo; K. Manivannan; C. Mariani; D. Marsden; J. Marshall; D. A. Martinez Caicedo; K. Mason; A. Mastbaum; N. McConkey; V. Meddage; T. Mettler; K. Miller; J. Mills; K. Mistry; A. Mogan; T. Mohayai; J. Moon; M. Mooney; A. F. Moor; C. D. Moore; L. Mora Lepin; J. Mousseau; M. Murphy; D. Naples; A. Navrer-Agasson; M. Nebot-Guinot; R. K. Neely; D. A. Newmark; J. Nowak; M. Nunes; O. Palamara; V. Paolone; A. Papadopoulou; V. Papavassiliou; S. F. Pate; N. Patel; A. Paudel; Z. Pavlovic; E. Piasetzky; I. D. Ponce-Pinto; S. Prince; X. Qian; J. L. Raaf; V. Radeka; A. Rafique; M. Reggiani-Guzzo; L. Ren; L. C.J. Rice; L. Rochester; J. Rodriguez Rondon; M. Rosenberg; M. Ross-Lonergan; G. Scanavini; D. W. Schmitz; A. Schukraft; W. Seligman; M. H. Shaevitz; R. Sharankova; J. Shi; J. Sinclair; A. Smith; E. L. Snider; M. Soderberg; S. Söldner-Rembold; P. Spentzouris; J. Spitz; M. Stancari; J. St.; T. Strauss; K. Sutton; S. Sword-Fehlberg; A. M. Szelc; N. Tagg; W. Tang; K. Terao; C. Thorpe; D. Totani; M. Toups; Y. T. Tsai; M. A. Uchida; T. Usher; W. Van De Pontseele; B. Viren; M. Weber; H. Wei; Z. Williams; S. Wolbers; T. Wongjirad; M. Wospakrik; K. Wresilo; N. Wright; W. Wu; E. Yandel; T. Yang; G. Yarbrough; L. E. Yates; H. W. Yu; G. P. Zeller; J. Zennamo; C. Zhang

doi:10.1088/1748-0221/16/12/T12017

Electromagnetic shower reconstruction and energy validation with Michel electrons and p⁰samples for the deep-learning-based analyses in MicroBooNE

P. Abratenko, R. An, J. Anthony, L. Arellano, J. Asaadi, A. Ashkenazi, S. Balasubramanian, B. Baller, C. Barnes, G. Barr, V. Basque, L. Bathe-Peters, O. Benevides Rodrigues, S. Berkman, A. Bhanderi, A. Bhat, M. Bishai, A. Blake, T. Bolton, J. Y. BookL. Camilleri, D. Caratelli, I. Caro Terrazas, R. Castillo Fernandez, F. Cavanna, G. Cerati, Y. Chen, D. Cianci, J. M. Conrad, M. Convery, L. Cooper-Troendle, J. I. Crespo-Anadón, M. Del Tutto, S. R. Dennis, P. Detje, A. Devitt, R. Diurba, R. Dorrill, K. Duffy, S. Dytman, B. Eberly, A. Ereditato, J. J. Evans, R. Fine, G. A. Fiorentini Aguirre, R. S. Fitzpatrick, B. T. Fleming, N. Foppiani, D. Franco, A. P. Furmanski, D. Garcia-Gamez, S. Gardiner, G. Ge, S. Gollapinni, O. Goodwin, E. Gramellini, P. Green, H. Greenlee, W. Gu, R. Guenette, P. Guzowski, L. Hagaman, O. Hen, C. Hilgenberg, G. A. Horton-Smith, A. Hourlier, R. Itay, C. James, X. Ji, L. Jiang, J. H. Jo, R. A. Johnson, Y. J. Jwa, D. Kalra, N. Kamp, N. Kaneshige, G. Karagiorgi, W. Ketchum, M. Kirby, T. Kobilarcik, I. Kreslo, R. Lazur, I. Lepetic, K. Li, Y. Li, K. Lin, B. R. Littlejohn, W. C. Louis, X. Luo, K. Manivannan, C. Mariani, D. Marsden, J. Marshall, D. A. Martinez Caicedo, K. Mason, A. Mastbaum, N. McConkey, V. Meddage, T. Mettler, K. Miller, J. Mills, K. Mistry, A. Mogan, T. Mohayai, J. Moon, M. Mooney, A. F. Moor, C. D. Moore, L. Mora Lepin, J. Mousseau, M. Murphy, D. Naples, A. Navrer-Agasson, M. Nebot-Guinot, R. K. Neely, D. A. Newmark, J. Nowak, M. Nunes, O. Palamara, V. Paolone, A. Papadopoulou, V. Papavassiliou, S. F. Pate, N. Patel, A. Paudel, Z. Pavlovic, E. Piasetzky, I. D. Ponce-Pinto, S. Prince, X. Qian, J. L. Raaf, V. Radeka, A. Rafique, M. Reggiani-Guzzo, L. Ren, L. C.J. Rice, L. Rochester, J. Rodriguez Rondon, M. Rosenberg, M. Ross-Lonergan, G. Scanavini, D. W. Schmitz, A. Schukraft, W. Seligman, M. H. Shaevitz, R. Sharankova, J. Shi, J. Sinclair, A. Smith, E. L. Snider, M. Soderberg, S. Söldner-Rembold, P. Spentzouris, J. Spitz, M. Stancari, J. St., T. Strauss, K. Sutton, S. Sword-Fehlberg, A. M. Szelc, N. Tagg, W. Tang, K. Terao, C. Thorpe, D. Totani, M. Toups, Y. T. Tsai, M. A. Uchida, T. Usher, W. Van De Pontseele, B. Viren, M. Weber, H. Wei, Z. Williams, S. Wolbers, T. Wongjirad, M. Wospakrik, K. Wresilo, N. Wright, W. Wu, E. Yandel, T. Yang, G. Yarbrough, L. E. Yates, H. W. Yu, G. P. Zeller, J. Zennamo, C. Zhang

Physics and Astronomy (Twin Cities)

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

5 Scopus citations

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 ?µ-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.

Original language	English (US)
Article number	T12017
Journal	Journal of Instrumentation
Volume	16
Issue number	12
DOIs	https://doi.org/10.1088/1748-0221/16/12/T12017
State	Published - Dec 2021

Bibliographical note

Publisher Copyright:
© 2021 IOP Publishing Ltd and Sissa Medialab.

Keywords

Neutrino detectors
Noble liquid detectors (scintillation, ionization, double-phase)
Pattern recognition, cluster finding, calibration and fitting methods
Time projection Chambers (TPC)

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10.1088/1748-0221/16/12/T12017

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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., ... Zhang, C. (2021). Electromagnetic shower reconstruction and energy validation with Michel electrons and p⁰samples for the deep-learning-based analyses in MicroBooNE. Journal of Instrumentation, 16(12), Article T12017. https://doi.org/10.1088/1748-0221/16/12/T12017

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, JY, Camilleri, L, Caratelli, D, Caro Terrazas, I, Castillo Fernandez, R, Cavanna, F, Cerati, G, Chen, Y, Cianci, D, Conrad, JM, Convery, M, Cooper-Troendle, L, Crespo-Anadón, JI, Del Tutto, M, Dennis, SR, Detje, P, Devitt, A, Diurba, R, Dorrill, R, Duffy, K, Dytman, S, Eberly, B, Ereditato, A, Evans, JJ, Fine, R, Fiorentini Aguirre, GA, Fitzpatrick, RS, Fleming, BT, Foppiani, N, Franco, D, Furmanski, AP, 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, GA, Hourlier, A, Itay, R, James, C, Ji, X, Jiang, L, Jo, JH, Johnson, RA, Jwa, YJ, 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, BR, Louis, WC, Luo, X, Manivannan, K, Mariani, C, Marsden, D, Marshall, J, Martinez Caicedo, DA, 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, AF, Moore, CD, Mora Lepin, L, Mousseau, J, Murphy, M, Naples, D, Navrer-Agasson, A, Nebot-Guinot, M, Neely, RK, Newmark, DA, Nowak, J, Nunes, M, Palamara, O, Paolone, V, Papadopoulou, A, Papavassiliou, V, Pate, SF, Patel, N, Paudel, A, Pavlovic, Z, Piasetzky, E, Ponce-Pinto, ID, Prince, S, Qian, X, Raaf, JL, Radeka, V, Rafique, A, Reggiani-Guzzo, M, Ren, L, Rice, LCJ, Rochester, L, Rodriguez Rondon, J, Rosenberg, M, Ross-Lonergan, M, Scanavini, G, Schmitz, DW, Schukraft, A, Seligman, W, Shaevitz, MH, Sharankova, R, Shi, J, Sinclair, J, Smith, A, Snider, EL, Soderberg, M, Söldner-Rembold, S, Spentzouris, P, Spitz, J, Stancari, M, St., J, Strauss, T, Sutton, K, Sword-Fehlberg, S, Szelc, AM, Tagg, N, Tang, W, Terao, K, Thorpe, C, Totani, D, Toups, M, Tsai, YT, Uchida, MA, 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, LE, Yu, HW, Zeller, GP, Zennamo, J & Zhang, C 2021, 'Electromagnetic shower reconstruction and energy validation with Michel electrons and p⁰samples for the deep-learning-based analyses in MicroBooNE', Journal of Instrumentation, vol. 16, no. 12, T12017. https://doi.org/10.1088/1748-0221/16/12/T12017

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title = "Electromagnetic shower reconstruction and energy validation with Michel electrons and p0samples for the deep-learning-based analyses in MicroBooNE",

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 ?µ-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.",

keywords = "Neutrino detectors, Noble liquid detectors (scintillation, ionization, double-phase), Pattern recognition, cluster finding, calibration and fitting methods, Time projection Chambers (TPC)",

author = "P. Abratenko and R. An and J. Anthony and L. Arellano and J. Asaadi and A. Ashkenazi and S. Balasubramanian and B. Baller and C. Barnes and G. Barr and V. Basque and L. Bathe-Peters and {Benevides Rodrigues}, O. and S. Berkman and A. Bhanderi and A. Bhat and M. Bishai and A. Blake and T. Bolton and Book, {J. Y.} and L. Camilleri and D. Caratelli and {Caro Terrazas}, I. and {Castillo Fernandez}, R. and F. Cavanna and G. Cerati and Y. Chen and D. Cianci and Conrad, {J. M.} and M. Convery and L. Cooper-Troendle and Crespo-Anad{\'o}n, {J. I.} and {Del Tutto}, M. and Dennis, {S. R.} and P. Detje and A. Devitt and R. Diurba and R. Dorrill and K. Duffy and S. Dytman and B. Eberly and A. Ereditato and Evans, {J. J.} and R. Fine and {Fiorentini Aguirre}, {G. A.} and Fitzpatrick, {R. S.} and Fleming, {B. T.} and N. Foppiani and D. Franco and Furmanski, {A. P.} and D. Garcia-Gamez and S. Gardiner and G. Ge and S. Gollapinni and O. Goodwin and E. Gramellini and P. Green and H. Greenlee and W. Gu and R. Guenette and P. Guzowski and L. Hagaman and O. Hen and C. Hilgenberg and Horton-Smith, {G. A.} and A. Hourlier and R. Itay and C. James and X. Ji and L. Jiang and Jo, {J. H.} and Johnson, {R. A.} and Jwa, {Y. J.} and D. Kalra and N. Kamp and N. Kaneshige and G. Karagiorgi and W. Ketchum and M. Kirby and T. Kobilarcik and I. Kreslo and R. Lazur and I. Lepetic and K. Li and Y. Li and K. Lin and Littlejohn, {B. R.} and Louis, {W. C.} and X. Luo and K. Manivannan and C. Mariani and D. Marsden and J. Marshall and {Martinez Caicedo}, {D. A.} and K. Mason and A. Mastbaum and N. McConkey and V. Meddage and T. Mettler and K. Miller and J. Mills and K. Mistry and A. Mogan and T. Mohayai and J. Moon and M. Mooney and Moor, {A. F.} and Moore, {C. D.} and {Mora Lepin}, L. and J. Mousseau and M. Murphy and D. Naples and A. Navrer-Agasson and M. Nebot-Guinot and Neely, {R. K.} and Newmark, {D. A.} and J. Nowak and M. Nunes and O. Palamara and V. Paolone and A. Papadopoulou and V. Papavassiliou and Pate, {S. F.} and N. Patel and A. Paudel and Z. Pavlovic and E. Piasetzky and Ponce-Pinto, {I. D.} and S. Prince and X. Qian and Raaf, {J. L.} and V. Radeka and A. Rafique and M. Reggiani-Guzzo and L. Ren and Rice, {L. C.J.} and L. Rochester and {Rodriguez Rondon}, J. and M. Rosenberg and M. Ross-Lonergan and G. Scanavini and Schmitz, {D. W.} and A. Schukraft and W. Seligman and Shaevitz, {M. H.} and R. Sharankova and J. Shi and J. Sinclair and A. Smith and Snider, {E. L.} and M. Soderberg and S. S{\"o}ldner-Rembold and P. Spentzouris and J. Spitz and M. Stancari and J. St. and T. Strauss and K. Sutton and S. Sword-Fehlberg and Szelc, {A. M.} and N. Tagg and W. Tang and K. Terao and C. Thorpe and D. Totani and M. Toups and Tsai, {Y. T.} and Uchida, {M. A.} and T. Usher and {Van De Pontseele}, W. and B. Viren and M. Weber and H. Wei and Z. Williams and S. Wolbers and T. Wongjirad and M. Wospakrik and K. Wresilo and N. Wright and W. Wu and E. Yandel and T. Yang and G. Yarbrough and Yates, {L. E.} and Yu, {H. W.} and Zeller, {G. P.} and J. Zennamo and C. Zhang",

note = "Publisher Copyright: {\textcopyright} 2021 IOP Publishing Ltd and Sissa Medialab.",

year = "2021",

month = dec,

doi = "10.1088/1748-0221/16/12/T12017",

language = "English (US)",

volume = "16",

journal = "Journal of Instrumentation",

issn = "1748-0221",

publisher = "IOP Publishing Ltd.",

number = "12",

}

TY - JOUR

T1 - Electromagnetic shower reconstruction and energy validation with Michel electrons and p0samples for the deep-learning-based analyses in MicroBooNE

AU - Abratenko, P.

AU - An, R.

AU - Anthony, J.

AU - Arellano, L.

AU - Asaadi, J.

AU - Ashkenazi, A.

AU - Balasubramanian, S.

AU - Baller, B.

AU - Barnes, C.

AU - Barr, G.

AU - Basque, V.

AU - Bathe-Peters, L.

AU - Benevides Rodrigues, O.

AU - Berkman, S.

AU - Bhanderi, A.

AU - Bhat, A.

AU - Bishai, M.

AU - Blake, A.

AU - Bolton, T.

AU - Book, J. Y.

AU - Camilleri, L.

AU - Caratelli, D.

AU - Caro Terrazas, I.

AU - Castillo Fernandez, R.

AU - Cavanna, F.

AU - Cerati, G.

AU - Chen, Y.

AU - Cianci, D.

AU - Conrad, J. M.

AU - Convery, M.

AU - Cooper-Troendle, L.

AU - Crespo-Anadón, J. I.

AU - Del Tutto, M.

AU - Dennis, S. R.

AU - Detje, P.

AU - Devitt, A.

AU - Diurba, R.

AU - Dorrill, R.

AU - Duffy, K.

AU - Dytman, S.

AU - Eberly, B.

AU - Ereditato, A.

AU - Evans, J. J.

AU - Fine, R.

AU - Fiorentini Aguirre, G. A.

AU - Fitzpatrick, R. S.

AU - Fleming, B. T.

AU - Foppiani, N.

AU - Franco, D.

AU - Furmanski, A. P.

AU - Garcia-Gamez, D.

AU - Gardiner, S.

AU - Ge, G.

AU - Gollapinni, S.

AU - Goodwin, O.

AU - Gramellini, E.

AU - Green, P.

AU - Greenlee, H.

AU - Gu, W.

AU - Guenette, R.

AU - Guzowski, P.

AU - Hagaman, L.

AU - Hen, O.

AU - Hilgenberg, C.

AU - Horton-Smith, G. A.

AU - Hourlier, A.

AU - Itay, R.

AU - James, C.

AU - Ji, X.

AU - Jiang, L.

AU - Jo, J. H.

AU - Johnson, R. A.

AU - Jwa, Y. J.

AU - Kalra, D.

AU - Kamp, N.

AU - Kaneshige, N.

AU - Karagiorgi, G.

AU - Ketchum, W.

AU - Kirby, M.

AU - Kobilarcik, T.

AU - Kreslo, I.

AU - Lazur, R.

AU - Lepetic, I.

AU - Li, K.

AU - Li, Y.

AU - Lin, K.

AU - Littlejohn, B. R.

AU - Louis, W. C.

AU - Luo, X.

AU - Manivannan, K.

AU - Mariani, C.

AU - Marsden, D.

AU - Marshall, J.

AU - Martinez Caicedo, D. A.

AU - Mason, K.

AU - Mastbaum, A.

AU - McConkey, N.

AU - Meddage, V.

AU - Mettler, T.

AU - Miller, K.

AU - Mills, J.

AU - Mistry, K.

AU - Mogan, A.

AU - Mohayai, T.

AU - Moon, J.

AU - Mooney, M.

AU - Moor, A. F.

AU - Moore, C. D.

AU - Mora Lepin, L.

AU - Mousseau, J.

AU - Murphy, M.

AU - Naples, D.

AU - Navrer-Agasson, A.

AU - Nebot-Guinot, M.

AU - Neely, R. K.

AU - Newmark, D. A.

AU - Nowak, J.

AU - Nunes, M.

AU - Palamara, O.

AU - Paolone, V.

AU - Papadopoulou, A.

AU - Papavassiliou, V.

AU - Pate, S. F.

AU - Patel, N.

AU - Paudel, A.

AU - Pavlovic, Z.

AU - Piasetzky, E.

AU - Ponce-Pinto, I. D.

AU - Prince, S.

AU - Qian, X.

AU - Raaf, J. L.

AU - Radeka, V.

AU - Rafique, A.

AU - Reggiani-Guzzo, M.

AU - Ren, L.

AU - Rice, L. C.J.

AU - Rochester, L.

AU - Rodriguez Rondon, J.

AU - Rosenberg, M.

AU - Ross-Lonergan, M.

AU - Scanavini, G.

AU - Schmitz, D. W.

AU - Schukraft, A.

AU - Seligman, W.

AU - Shaevitz, M. H.

AU - Sharankova, R.

AU - Shi, J.

AU - Sinclair, J.

AU - Smith, A.

AU - Snider, E. L.

AU - Soderberg, M.

AU - Söldner-Rembold, S.

AU - Spentzouris, P.

AU - Spitz, J.

AU - Stancari, M.

AU - St., J.

AU - Strauss, T.

AU - Sutton, K.

AU - Sword-Fehlberg, S.

AU - Szelc, A. M.

AU - Tagg, N.

AU - Tang, W.

AU - Terao, K.

AU - Thorpe, C.

AU - Totani, D.

AU - Toups, M.

AU - Tsai, Y. T.

AU - Uchida, M. A.

AU - Usher, T.

AU - Van De Pontseele, W.

AU - Viren, B.

AU - Weber, M.

AU - Wei, H.

AU - Williams, Z.

AU - Wolbers, S.

AU - Wongjirad, T.

AU - Wospakrik, M.

AU - Wresilo, K.

AU - Wright, N.

AU - Wu, W.

AU - Yandel, E.

AU - Yang, T.

AU - Yarbrough, G.

AU - Yates, L. E.

AU - Yu, H. W.

AU - Zeller, G. P.

AU - Zennamo, J.

AU - Zhang, C.

PY - 2021/12

Y1 - 2021/12

N2 - 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 ?µ-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.

AB - 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 ?µ-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.

KW - Neutrino detectors

KW - Noble liquid detectors (scintillation, ionization, double-phase)

KW - Pattern recognition, cluster finding, calibration and fitting methods

KW - Time projection Chambers (TPC)

UR - http://www.scopus.com/inward/record.url?scp=85122872870&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85122872870&partnerID=8YFLogxK

U2 - 10.1088/1748-0221/16/12/T12017

DO - 10.1088/1748-0221/16/12/T12017

M3 - Article

AN - SCOPUS:85122872870

SN - 1748-0221

VL - 16

JO - Journal of Instrumentation

JF - Journal of Instrumentation

IS - 12

M1 - T12017

ER -