Neutral pion reconstruction using machine learning in the MINERvA experiment at $\langle E_\nu \rangle \sim 6$ GeV

TL;DR

This paper introduces a machine learning-based semantic segmentation method for neutral pion reconstruction in MINERvA data, significantly improving purity and efficiency, and applicable to future neutrino experiments with liquid-argon detectors.

Contribution

It presents a novel machine learning technique for neutral pion reconstruction that enhances purity and efficiency in neutrino experiments, applicable to modern tracking calorimeters.

Findings

Purity of neutral pion reconstruction increased from 71% to 89%.

Reconstruction efficiency improved by approximately 40%.

Method applicable to future neutrino detectors like liquid-argon TPCs.

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.