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We have developed a convolutional neural network (CNN) that can make apixel-level prediction of objects in image data recorded by a liquid argon timeprojection chamber (LArTPC) for the first time. We describe the network design,training techniques, and software tools developed to train this network. Thegoal of this work is to develop a complete deep neural network based datareconstruction chain for the MicroBooNE detector. We show the firstdemonstration of a network's validity on real LArTPC data using MicroBooNEcollection plane images. The demonstration is performed for stopping muon and a$\nu_\mu$ charged current neutral pion data samples.

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