Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber
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American Physical Society
Abratenko, P... [et al.] (2021). Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber. Physical Review D, 103(9), 092003. DOI: [10.1103/PhysRevD.103.092003]
SponsorshipFermi Research Alliance, LLC (FRA) DE-AC02-07CH11359; United States Department of Energy (DOE); National Science Foundation (NSF); Swiss National Science Foundation (SNSF) European Commission; Science and Technology Facilities Council (STFC), United Kingdom Research and Innovation; Royal Society of London
We present the multiple particle identification (MPID) network, a convolutional neural network for multiple object classification, developed by MicroBooNE. MPID provides the probabilities that an interaction includes an e(-), gamma, mu(-), pi(+/-), and protons in a liquid argon time projection chamber single readout plane. The network extends the single particle identification network previously developed by MicroBooNE [Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber, R. Acciarri et al. J. Instrum. 12, P03011 (2017)]. 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.e search analysis. In this paper, we present the network's design, training, and performance on simulation and data from the MicroBooNE detector.