학술논문

Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber
Document Type
article
Source
Physical Review D. 99(9)
Subject
Nuclear and Plasma Physics
Physical Sciences
Neurosciences
hep-ex
cs.CV
physics.data-an
physics.ins-det
Astronomical and Space Sciences
Atomic
Molecular
Nuclear
Particle and Plasma Physics
Quantum Physics
Nuclear & Particles Physics
Mathematical physics
Astronomical sciences
Particle and high energy physics
Language
Abstract
We have developed a convolutional neural network that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a νμ charged-current neutral pion data samples.