Augmented Signal Processing in Liquid Argon Time Projection Chambers with a Deep Neural Network

TL;DR

This paper introduces a deep neural network approach to enhance signal processing in Liquid Argon Time Projection Chambers, significantly improving the detection of charge signals in neutrino detectors through combined domain knowledge and machine learning.

Contribution

The paper presents the first application of deep learning to LArTPC signal processing, integrating domain expertise to improve signal region detection over traditional methods.

Findings

Significant improvement in signal detection accuracy.

Effective combination of domain knowledge and deep learning.

Validated performance with realistic detector simulations.

Abstract

The Liquid Argon Time Projection Chamber (LArTPC) is an advanced neutrino detector technology widely used in recent and upcoming accelerator neutrino experiments. It features a low energy threshold and high spatial resolution that allow for comprehensive reconstruction of event topologies. In current-generation LArTPCs, the recorded data consist of digitized waveforms on wires produced by induced signal on wires of drifting ionization electrons, which can also be viewed as two-dimensional (2D) (time versus wire) projection images of charged-particle trajectories. For such an imaging detector, one critical step is the signal processing that reconstructs the original charge projections from the recorded 2D images. For the first time, we introduce a deep neural network in LArTPC signal processing to improve the signal region of interest detection. By combining domain knowledge (e.g., matching information from multiple wire planes) and deep learning, this method shows significant improvements over traditional methods. This work details the method, software tools, and performance evaluated with realistic detector simulations.