Neutral pion reconstruction using machine learning in the MINERvA experiment at 〈Ev〉 ∼ 6GeV

A. Ghosh; B. Yaeggy; R. Galindo; Z. Ahmad Dar; F. Akbar; M. V. Ascencio; A. Bashyal; A. Bercellie; J. L. Bonilla; G. Caceres; T. Cai; M. F. Carneiro; H. da Motta; G. A. Díaz; J. Felix; A. Filkins; R. Fine; A. M. Gago; T. Golan; R. Gran; D. A. Harris; S. Henry; S. Jena; D. Jena; J. Kleykamp; M. Kordosky; D. Last; T. Le; A. Lozano; X. G. Lu; E. Maher; S. Manly; W. A. Mann; C. Mauger; K. S. McFarland; B. Messerly; J. Miller; L. M. Montano; D. Naples; J. K. Nelson; C. Nguyen; A. Olivier; V. Paolone; G. N. Perdue; M. A. Ramírez; H. Ray; D. Ruterbories; C. J.Solano Salinas; H. Su; M. Sultana; V. S. Syrotenko; E. Valencia; M. Wospakrik; C. Wret; K. Yang; L. Zazueta

doi:10.1088/1748-0221/16/07/P07060

Neutral pion reconstruction using machine learning in the MINERvA experiment at 〈E_v〉 ∼ 6GeV

A. Ghosh, B. Yaeggy, R. Galindo, Z. Ahmad Dar, F. Akbar, M. V. Ascencio, A. Bashyal, A. Bercellie, J. L. Bonilla, G. Caceres, T. Cai, M. F. Carneiro, H. da Motta, G. A. Díaz, J. Felix, A. Filkins, R. Fine, A. M. Gago, T. Golan, R. GranD. A. Harris, S. Henry, S. Jena, D. Jena, J. Kleykamp, M. Kordosky, D. Last, T. Le, A. Lozano, X. G. Lu, E. Maher, S. Manly, W. A. Mann, C. Mauger, K. S. McFarland, B. Messerly, J. Miller, L. M. Montano, D. Naples, J. K. Nelson, C. Nguyen, A. Olivier, V. Paolone, G. N. Perdue, M. A. Ramírez, H. Ray, D. Ruterbories, C. J.Solano Salinas, H. Su, M. Sultana, V. S. Syrotenko, E. Valencia, M. Wospakrik, C. Wret, K. Yang, L. Zazueta

Physics & Astronomy (Duluth)

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

Abstract

This paper presents a novel neutral-pion reconstruction that takes advantage of the machine learning technique of semantic segmentation using MINERvA data collected between 2013-2017, with an average neutrino energy of 6 GeV. Semantic segmentation improves the purity of neutral pion reconstruction from two γs from 70.7 ± 0.9% to 89.3 ± 0.7% and improves the efficiency of the reconstruction by approximately 40%. We demonstrate our method in a charged current neutral pion production analysis where a single neutral pion is reconstructed. This technique is applicable to modern tracking calorimeters, such as the new generation of liquid-argon time projection chambers, exposed to neutrino beams with 〈E_v〉 between 1-10 GeV. In such experiments it can facilitate the identification of ionization hits which are associated with electromagnetic showers, thereby enabling improved reconstruction of charged-current v_e events arising from v_μ → v_e appearance.

Original language	English (US)
Article number	P07060
Journal	Journal of Instrumentation
Volume	16
Issue number	7
DOIs	https://doi.org/10.1088/1748-0221/16/07/P07060
State	Published - Jul 2021

Bibliographical note

Publisher Copyright:
© 2021 IOP Publishing Ltd and Sissa Medialab

Keywords

Analysis and statistical methods
Calibration
Cluster finding
Fitting methods
Neutrino detectors
Pattern recognition

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Ghosh, A., Yaeggy, B., Galindo, R., Dar, Z. A., Akbar, F., Ascencio, M. V., Bashyal, A., Bercellie, A., Bonilla, J. L., Caceres, G., Cai, T., Carneiro, M. F., da Motta, H., Díaz, G. A., Felix, J., Filkins, A., Fine, R., Gago, A. M., Golan, T., ... Zazueta, L. (2021). Neutral pion reconstruction using machine learning in the MINERvA experiment at 〈E_v〉 ∼ 6GeV. Journal of Instrumentation, 16(7), Article P07060. https://doi.org/10.1088/1748-0221/16/07/P07060

Ghosh, A, Yaeggy, B, Galindo, R, Dar, ZA, Akbar, F, Ascencio, MV, Bashyal, A, Bercellie, A, Bonilla, JL, Caceres, G, Cai, T, Carneiro, MF, da Motta, H, Díaz, GA, Felix, J, Filkins, A, Fine, R, Gago, AM, Golan, T, Gran, R, Harris, DA, Henry, S, Jena, S, Jena, D, Kleykamp, J, Kordosky, M, Last, D, Le, T, Lozano, A, Lu, XG, Maher, E, Manly, S, Mann, WA, Mauger, C, McFarland, KS, Messerly, B, Miller, J, Montano, LM, Naples, D, Nelson, JK, Nguyen, C, Olivier, A, Paolone, V, Perdue, GN, Ramírez, MA, Ray, H, Ruterbories, D, Salinas, CJS, Su, H, Sultana, M, Syrotenko, VS, Valencia, E, Wospakrik, M, Wret, C, Yang, K & Zazueta, L 2021, 'Neutral pion reconstruction using machine learning in the MINERvA experiment at 〈E_v〉 ∼ 6GeV', Journal of Instrumentation, vol. 16, no. 7, P07060. https://doi.org/10.1088/1748-0221/16/07/P07060

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abstract = "This paper presents a novel neutral-pion reconstruction that takes advantage of the machine learning technique of semantic segmentation using MINERvA data collected between 2013-2017, with an average neutrino energy of 6 GeV. Semantic segmentation improves the purity of neutral pion reconstruction from two γs from 70.7 ± 0.9% to 89.3 ± 0.7% and improves the efficiency of the reconstruction by approximately 40%. We demonstrate our method in a charged current neutral pion production analysis where a single neutral pion is reconstructed. This technique is applicable to modern tracking calorimeters, such as the new generation of liquid-argon time projection chambers, exposed to neutrino beams with 〈Ev〉 between 1-10 GeV. In such experiments it can facilitate the identification of ionization hits which are associated with electromagnetic showers, thereby enabling improved reconstruction of charged-current ve events arising from vμ → ve appearance.",

keywords = "Analysis and statistical methods, Calibration, Cluster finding, Fitting methods, Neutrino detectors, Pattern recognition",

author = "A. Ghosh and B. Yaeggy and R. Galindo and Dar, {Z. Ahmad} and F. Akbar and Ascencio, {M. V.} and A. Bashyal and A. Bercellie and Bonilla, {J. L.} and G. Caceres and T. Cai and Carneiro, {M. F.} and {da Motta}, H. and D{\'i}az, {G. A.} and J. Felix and A. Filkins and R. Fine and Gago, {A. M.} and T. Golan and R. Gran and Harris, {D. A.} and S. Henry and S. Jena and D. Jena and J. Kleykamp and M. Kordosky and D. Last and T. Le and A. Lozano and Lu, {X. G.} and E. Maher and S. Manly and Mann, {W. A.} and C. Mauger and McFarland, {K. S.} and B. Messerly and J. Miller and Montano, {L. M.} and D. Naples and Nelson, {J. K.} and C. Nguyen and A. Olivier and V. Paolone and Perdue, {G. N.} and Ram{\'i}rez, {M. A.} and H. Ray and D. Ruterbories and Salinas, {C. J.Solano} and H. Su and M. Sultana and Syrotenko, {V. S.} and E. Valencia and M. Wospakrik and C. Wret and K. Yang and L. Zazueta",

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doi = "10.1088/1748-0221/16/07/P07060",

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TY - JOUR

T1 - Neutral pion reconstruction using machine learning in the MINERvA experiment at 〈Ev〉 ∼ 6GeV

AU - Ghosh, A.

AU - Yaeggy, B.

AU - Galindo, R.

AU - Dar, Z. Ahmad

AU - Akbar, F.

AU - Ascencio, M. V.

AU - Bashyal, A.

AU - Bercellie, A.

AU - Bonilla, J. L.

AU - Caceres, G.

AU - Cai, T.

AU - Carneiro, M. F.

AU - da Motta, H.

AU - Díaz, G. A.

AU - Felix, J.

AU - Filkins, A.

AU - Fine, R.

AU - Gago, A. M.

AU - Golan, T.

AU - Gran, R.

AU - Harris, D. A.

AU - Henry, S.

AU - Jena, S.

AU - Jena, D.

AU - Kleykamp, J.

AU - Kordosky, M.

AU - Last, D.

AU - Le, T.

AU - Lozano, A.

AU - Lu, X. G.

AU - Maher, E.

AU - Manly, S.

AU - Mann, W. A.

AU - Mauger, C.

AU - McFarland, K. S.

AU - Messerly, B.

AU - Miller, J.

AU - Montano, L. M.

AU - Naples, D.

AU - Nelson, J. K.

AU - Nguyen, C.

AU - Olivier, A.

AU - Paolone, V.

AU - Perdue, G. N.

AU - Ramírez, M. A.

AU - Ray, H.

AU - Ruterbories, D.

AU - Salinas, C. J.Solano

AU - Su, H.

AU - Sultana, M.

AU - Syrotenko, V. S.

AU - Valencia, E.

AU - Wospakrik, M.

AU - Wret, C.

AU - Yang, K.

AU - Zazueta, L.

PY - 2021/7

Y1 - 2021/7

N2 - This paper presents a novel neutral-pion reconstruction that takes advantage of the machine learning technique of semantic segmentation using MINERvA data collected between 2013-2017, with an average neutrino energy of 6 GeV. Semantic segmentation improves the purity of neutral pion reconstruction from two γs from 70.7 ± 0.9% to 89.3 ± 0.7% and improves the efficiency of the reconstruction by approximately 40%. We demonstrate our method in a charged current neutral pion production analysis where a single neutral pion is reconstructed. This technique is applicable to modern tracking calorimeters, such as the new generation of liquid-argon time projection chambers, exposed to neutrino beams with 〈Ev〉 between 1-10 GeV. In such experiments it can facilitate the identification of ionization hits which are associated with electromagnetic showers, thereby enabling improved reconstruction of charged-current ve events arising from vμ → ve appearance.

AB - This paper presents a novel neutral-pion reconstruction that takes advantage of the machine learning technique of semantic segmentation using MINERvA data collected between 2013-2017, with an average neutrino energy of 6 GeV. Semantic segmentation improves the purity of neutral pion reconstruction from two γs from 70.7 ± 0.9% to 89.3 ± 0.7% and improves the efficiency of the reconstruction by approximately 40%. We demonstrate our method in a charged current neutral pion production analysis where a single neutral pion is reconstructed. This technique is applicable to modern tracking calorimeters, such as the new generation of liquid-argon time projection chambers, exposed to neutrino beams with 〈Ev〉 between 1-10 GeV. In such experiments it can facilitate the identification of ionization hits which are associated with electromagnetic showers, thereby enabling improved reconstruction of charged-current ve events arising from vμ → ve appearance.

KW - Analysis and statistical methods

KW - Calibration

KW - Cluster finding

KW - Fitting methods

KW - Neutrino detectors

KW - Pattern recognition

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U2 - 10.1088/1748-0221/16/07/P07060

DO - 10.1088/1748-0221/16/07/P07060

M3 - Article

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SN - 1748-0221

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