A deep-learning based raw waveform region-of-interest finder for the liquid argon time projection chamber

TL;DR

This paper introduces a deep learning algorithm using CNNs to identify regions of interest in raw waveforms from liquid argon TPC detectors, significantly improving low-energy signal detection efficiency.

Contribution

It presents a novel CNN-based ROI finder tailored for LArTPC data, enhancing low-energy event detection over traditional methods.

Findings

Approximately doubled efficiency in low-energy signal extraction.

Effective noise mitigation and realistic data modeling.

Promising results for low-energy physics exploration.

Abstract

The liquid argon time projection chamber (LArTPC) detector technology has an excellent capability to measure properties of low-energy neutrinos produced by the sun and supernovae and to look for exotic physics at very low energies. In order to achieve those physics goals, it is crucial to identify and reconstruct signals in the waveforms recorded on each TPC wire. In this paper, we report on a novel algorithm based on a one-dimensional convolutional neural network (CNN) to look for the region-of-interest (ROI) in raw waveforms. We test this algorithm using data from the ArgoNeuT experiment in conjunction with an improved noise mitigation procedure and a more realistic data-driven noise model for simulated events. This deep-learning ROI finder shows promising performance in extracting small signals and gives an efficiency approximately twice that of the traditional algorithm in the low energy region of $\sim$0.03-0.1 MeV. This method offers great potential to explore low-energy physics using LArTPCs.