Enhancing Neutrino Event Reconstruction with Pixel-Based 3D Readout for Liquid Argon Time Projection Chambers

TL;DR

This paper demonstrates that 3D pixelated readouts in liquid argon TPCs improve neutrino event reconstruction over traditional 2D wire readouts, especially for complex event topologies, enhancing future experimental capabilities.

Contribution

The study provides a comparative analysis showing the advantages of 3D pixel readouts over 2D wire readouts in neutrino event reconstruction using deep learning methods.

Findings

3D readout improves reconstruction efficiency and purity.

3D readout is particularly effective for complex event topologies.

3D pixelated detectors could significantly enhance future neutrino experiments.

Abstract

In this paper we explore the potential improvements in neutrino event reconstruction that a 3D pixelated readout could offer over a 2D projective wire readout for liquid argon time projection chambers. We simulate and study events in two generic, idealized detector configurations for these two designs, classifying events in each sample with deep convolutional neural networks to compare the best 2D results to the best 3D results. In almost all cases we find that the 3D readout provides better reconstruction efficiency and purity than the 2D projective wire readout, with the advantages of 3D being particularly evident in more complex topologies, such as electron neutrino charged current events. We conclude that the use of a 3D pixelated detector could significantly enhance the reach and impact of future liquid argon TPC experiments physics program, such as DUNE.