@misc{10481/69315,
year = {2021},
month = {5},
url = {http://hdl.handle.net/10481/69315},
abstract = {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.},
organization = {Fermi Research Alliance, LLC (FRA) 

DE-AC02-07CH11359},
organization = {United States Department of Energy (DOE)},
organization = {National Science Foundation (NSF)},
organization = {Swiss National Science Foundation (SNSF)

European Commission},
organization = {Science and Technology Facilities Council (STFC), United Kingdom Research and Innovation},
organization = {Royal Society of London},
publisher = {American Physical Society},
title = {Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber},
doi = {10.1103/PhysRevD.103.092003},
author = {Abratenko, P. and García Gámez, Diego and MicroBooNE Collaboration},
}