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.
Original language | English (US) |
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Article number | P07060 |
Journal | Journal of Instrumentation |
Volume | 16 |
Issue number | 7 |
DOIs | |
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