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A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber

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Published
Article number092003
<mark>Journal publication date</mark>1/05/2021
<mark>Journal</mark>Physical Review D
Issue number9
Volume103
Number of pages32
Publication StatusPublished
<mark>Original language</mark>English

Abstract

We present the multiple particle identification (MPID) network, a convolutional neural network (CNN) for multiple object classification, developed by MicroBooNE. MPID provides the probabilities of $e^-$, $\gamma$, $\mu^-$, $\pi^\pm$, and protons in a single liquid argon time projection chamber (LArTPC) readout plane. The network extends the single particle identification network previously developed by MicroBooNE. MPID takes as input an image either cropped around a reconstructed interaction vertex or containing only activity connected to a reconstructed vertex, therefore relieving the tool from inefficiencies in vertex finding and particle clustering. The network serves as an important component in MicroBooNE's deep learning based $\nu_e$ search analysis. In this paper, we present the network's design, training, and performance on simulation and data from the MicroBooNE detector.