Neutrino interaction classification with a convolutional neural network in the DUNE far detector

TL;DR

This paper presents a convolutional neural network approach for classifying neutrino interactions in the DUNE detector, achieving high efficiency and purity to enhance $CP$-violation measurements.

Contribution

It introduces a deep learning method that significantly improves the selection efficiency and purity of neutrino interaction events in the DUNE experiment.

Findings

Electron neutrino selection efficiency peaks at 90%.

Muon neutrino selection efficiency exceeds 96%.

Selection purity of 90% for electron neutrino interactions.

Abstract

The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure $CP$-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2-5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino energies above 2 GeV. When considering all electron neutrino and antineutrino interactions as signal, a selection purity of 90% is achieved. These event selections are critical to maximize the sensitivity of the experiment to $CP$-violating effects.