| Summary: | 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.
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