Cosmic ray muon clustering for the MicroBooNE liquid argon time projection chamber using sMask-RCNN

TL;DR

This paper presents sMask-RCNN, a modified neural network for cosmic ray muon clustering in liquid argon TPCs, improving processing speed and effectively vetoing cosmic ray backgrounds in neutrino detection.

Contribution

The paper introduces sMask-RCNN, a sparse convolution-based neural network that enhances cosmic ray muon clustering efficiency and background rejection in liquid argon TPC data.

Findings

sMask-RCNN achieves 85.9% pixel clustering efficiency

Removes 70% of cosmic ray muon background events

Maintains 80.1% electron neutrino signal efficiency

Abstract

In this article, we describe a modified implementation of Mask Region-based Convolutional Neural Networks (Mask-RCNN) for cosmic ray muon clustering in a liquid argon TPC and applied to MicroBooNE neutrino data. Our implementation of this network, called sMask-RCNN, uses sparse submanifold convolutions to increase processing speed on sparse datasets, and is compared to the original dense version in several metrics. The networks are trained to use wire readout images from the MicroBooNE liquid argon time projection chamber as input and produce individually labeled particle interactions within the image. These outputs are identified as either cosmic ray muon or electron neutrino interactions. We find that sMask-RCNN has an average pixel clustering efficiency of 85.9% compared to the dense network's average pixel clustering efficiency of 89.1%. We demonstrate the ability of sMask-RCNN used in conjunction with MicroBooNE's state-of-the-art Wire-Cell cosmic tagger to veto events containing only cosmic ray muons. The addition of sMask-RCNN to the Wire-Cell cosmic tagger removes 70% of the remaining cosmic ray muon background events at the same electron neutrino event signal efficiency. This event veto can provide 99.7% rejection of cosmic ray-only background events while maintaining an electron neutrino event-level signal efficiency of 80.1%. In addition to cosmic ray muon identification, sMask-RCNN could be used to extract features and identify different particle interaction types in other 3D-tracking detectors.