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

, MicroBooNE Collaboration and Blake, A. and Devitt, D. and Nowak, J. and Thorpe, C. (2021) A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber. Physical Review D, 103 (9). ISSN 1550-7998

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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.

Item Type:
Journal Article
Journal or Publication Title:
Physical Review D
Subjects:
ID Code:
153219
Deposited By:
Deposited On:
29 Mar 2021 09:00
Refereed?:
Yes
Published?:
Published
Last Modified:
23 Jun 2021 04:12