A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber
This work addresses particle identification in neutrino physics experiments, representing an incremental step toward a deep learning-based reconstruction chain.
The researchers tackled the problem of pixel-level electromagnetic particle identification in MicroBooNE LArTPC image data by developing a convolutional neural network, achieving the first demonstration of its validity on real data for stopping muon and νμ charged current neutral pion samples.
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. The demonstration is performed for stopping muon and a $ν_μ$ charged current neutral pion data samples.