Wire-Cell 3D Pattern Recognition Techniques for Neutrino Event Reconstruction in Large LArTPCs: Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation

TL;DR

This paper presents Wire-Cell, a comprehensive 3D pattern recognition framework for liquid argon TPCs, utilizing advanced algorithms and deep learning to improve neutrino event reconstruction accuracy and efficiency.

Contribution

It introduces a novel 3D pattern recognition pipeline with a deep neural network for vertex reconstruction, significantly enhancing neutrino event analysis in large LArTPCs.

Findings

Deep neural network increases vertex efficiency by 30% for $ u_e$ interactions.

Achieves 80-90% reconstruction efficiency for primary leptons.

Provides 15-20% energy resolution for charged-current neutrino interactions.

Abstract

Wire-Cell is a 3D event reconstruction package for liquid argon time projection chambers. Through geometry, time, and drifted charge from multiple readout wire planes, 3D space points with associated charge are reconstructed prior to the pattern recognition stage. Pattern recognition techniques, including track trajectory and $dQ/dx$ (ionization charge per unit length) fitting, 3D neutrino vertex fitting, track and shower separation, particle-level clustering, and particle identification are then applied on these 3D space points as well as the original 2D projection measurements. A deep neural network is developed to enhance the reconstruction of the neutrino interaction vertex. Compared to traditional algorithms, the deep neural network boosts the vertex efficiency by a relative 30\% for charged-current $\nu_e$ interactions. This pattern recognition achieves 80-90\% reconstruction efficiencies for primary leptons, after a 65.8\% (72.9\%) vertex efficiency for charged-current $\nu_e$ ($\nu_\mu$) interactions. Based on the resulting reconstructed particles and their kinematics, we also achieve 15-20\% energy reconstruction resolutions for charged-current neutrino interactions.