Neutral pion reconstruction using machine learning in the experiment at ⟨Eν⟩ 6 GeV. (29th July 2021) - Ghosh, A.; Yaeggy, B.; Galindo, R.; Ahmad Dar, Z.; Akbar, F.; Ascencio, M.V.; Bashyal, A.; Bercellie, A.; Bonilla, J.L.; Caceres, G.; Cai, T.; Carneiro, M.F.; et al.
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 ν ⟩ 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 νe events arising from νμ  → νe appearance.