| 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 cite{ub_singlePID}.
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 $
u_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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