Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber

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 ${e}^{\ensuremath{-}}$, $\ensuremath{\gamma}$, ${\ensuremath{\mu}}^{\ensuremath{-}}$, ${\ensuremath{\pi}}^{\ifmmode\pm\else\textpm\fi{}}$, 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 ${\ensuremath{\nu}}_{e}$ search analysis. In this paper, we present the network's design, training, and performance on simulation and data from the MicroBooNE detector.