DNN-based Signal Processing for Liquid Argon Time Projection Chambers

TL;DR

This paper presents a deep learning approach using U-ResNet for signal processing in liquid argon time projection chambers, improving ROI identification amidst complex detector effects for neutrino physics applications.

Contribution

Introduces a novel deep learning-based method for ROI detection in LArTPCs, outperforming traditional techniques and robust across various detector conditions.

Findings

Significant performance improvement over traditional methods

Robustness across diverse detector effects

Adoption in neutrino experiment signal processing

Abstract

We investigate a deep learning-based signal processing for liquid argon time projection chambers (LArTPCs), a leading detector technology in neutrino physics. Identifying regions of interest (ROIs) in LArTPCs is challenging due to signal cancellation from bipolar responses and various detector effects observed in real data. We approach ROI identification as an image segmentation task, and employ a U-ResNet architecture. The network is trained on samples that incorporate detector geometry information and include a range of detector variations. Our approach significantly outperforms traditional methods while maintaining robustness across diverse detector conditions. This method has been adopted for signal processing in the Short-Baseline Neutrino program and provides a valuable foundation for future experiments such as the Deep Underground Neutrino Experiment.