학술논문

Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber
Document Type
article
Source
Journal of Instrumentation. 12(3)
Subject
Analysis and statistical methods
Particle identification methods
Image filtering
Time projection chambers
physics.ins-det
hep-ex
Nuclear & Particles Physics
Physical Sciences
Engineering
Language
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.