Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

TL;DR

This paper presents a convolutional neural network algorithm that effectively classifies energy deposits in the ProtoDUNE-SP detector as track-like or shower-like, aiding neutrino particle identification and energy reconstruction.

Contribution

The paper introduces a novel CNN-based method for classifying energy deposits in liquid argon TPC data, demonstrating high efficiency and consistency between data and simulation.

Findings

High classification efficiency for track- and shower-like particles

Consistent performance between experimental data and simulation

Effective identification of Michel electrons

Abstract

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation.