학술논문
Neutral pion reconstruction using machine learning in the MINERvA experiment at $\langle E_\nu \rangle \sim 6$ GeV
Document Type
Working Paper
Author
Ghosh, A.; Yaeggy, B.; Galindo, R.; Dar, Z. Ahmad; 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.; Gran, R.; Harris, D. A.; Henry, S.; Jena, S.; Jena, D.; Kleykamp, J.; Kordosky, M.; Last, D.; Len, T.; Lozano, A.; Lu, X. -G.; Maher, E.; Manly, S.; Mann, W. A.; Mauger, C.; McFarland, K. S.; Messerly, B.; Miller, J.; Montano, Luis M.; Naples, D.; Nelson, J. K.; Nguyen, C.; Olivier, A.; Paolone, V.; Perdue, G. N.; Ramírez, M. A.; Ray, H.; Ruterbories, D.; Salinas, C. J. Solano; Su, H.; Sultana, M.; Syrotenko, V. S.; Valencia, E.; Wospakrik, M.; Wret, C.; Yang, K.; Zazueta, L.
Source
JINST 16 P07060 2021
Subject
Language
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 gammas from 71\% to 89\% 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 $\langle E_\nu \rangle$ 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 $\nu_e$ events arising from $\nu_{\mu} \rightarrow \nu_{e}$ appearance.
Comment: 26 pages, v2 matches published version
Comment: 26 pages, v2 matches published version