Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber

TL;DR

This paper explores the application of convolutional neural networks to analyze neutrino events in a liquid argon detector, demonstrating their effectiveness in particle classification, localization, and event detection amidst background noise.

Contribution

It introduces CNN-based methods tailored for LArTPC data, addressing technical challenges and showcasing their potential for neutrino event analysis.

Findings

CNNs effectively classify particle images

Localization of neutrino interactions is feasible

Detection of neutrino events amidst cosmic backgrounds

Abstract

We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.