A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber

Understand

We have developed a convolutional neural network (CNN) 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.