March 14, 2017
Journal Article

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

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 identifi-cation 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.

Revised: January 25, 2021 | Published: March 14, 2017

Citation

Acciarri R., C. Adams, R.A. An, J. Asaadi, M. Auger, L. Bagby, and B. Baller, et al. 2017. Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber. Journal of Instrumentation 12, no. 3:Article No. P0301. PNNL-SA-123442. doi:10.1088/1748-0221/12/03/P03011