# Context-Enriched Identification of Particles with a Convolutional Network for Neutrino Events

## TL;DR

This paper introduces a novel convolutional neural network that leverages contextual information to improve particle identification accuracy in neutrino detector data, achieving over 83% efficiency and purity.

## Contribution

It presents the first CNN with a four-tower siamese architecture that incorporates context for particle classification in neutrino interactions.

## Key findings

- Context information improves classification performance.
- Achieved 83.3% efficiency and 83.5% purity.
- First CNN to use this architecture for neutrino particle ID.

## Abstract

Particle detectors record the interactions of subatomic particles and their passage through matter. The identification of these particles is necessary for in-depth physics analysis. While particles can be identified by their individual behavior as they travel through matter, the full context of the interaction in which they are produced can aid the classification task substantially. We have developed the first convolutional neural network for particle identification which uses context information. This is also the first implementation of a four-tower siamese-type architecture both for separation of independent inputs and inclusion of context information. The network classifies clusters of energy deposits from the NOvA neutrino detectors as electrons, muons, photons, pions, and protons with an overall efficiency and purity of 83.3% and 83.5%, respectively. We show that providing the network with context information improves performance by comparing our results with a network trained without context information.