A Convolutional Neural Network Neutrino Event Classifier

TL;DR

This paper presents a CNN-based classifier for neutrino interactions in calorimeters, demonstrating improved performance over existing methods in the NOvA experiment.

Contribution

It introduces a novel CNN application called CVN for neutrino event classification, bypassing detailed reconstruction and enhancing accuracy.

Findings

CVN outperforms previous algorithms in neutrino event identification

The method simplifies analysis by using topology-based classification

Demonstrates effectiveness in the NOvA detector environment

Abstract

Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.