Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE

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Abstract

We present the performance of a semantic segmentation network, sparsessnet, that provides pixel-level classification of MicroBooNE data. The MicroBooNE experiment employs a liquid argon time projection chamber for the study of neutrino properties and interactions. sparsessnet is a submanifold sparse convolutional neural network, which provides the initial machine learning based algorithm utilized in one of MicroBooNEs ${\ensuremath{\nu}}_{e}$-appearance oscillation analyses. The network is trained to categorize pixels into five classes, which are reclassified into two classes more relevant to the current analysis. The output of sparsessnet is a key input in further analysis steps. This technique, used for the first time in liquid argon time projection chambers data and is an improvement compared to a previously used convolutional neural network, both in accuracy and computing resource utilization. The accuracy achieved on the test sample is $\ensuremath{\ge}99%$. For full neutrino interaction simulations, the time for processing one image is $\ensuremath{\approx}0.5\text{ }\text{ }\mathrm{sec}$, the memory usage is at 1 GB level, which allows utilization of most typical CPU worker machine.

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