Wire-cell 3D pattern recognition techniques for neutrino event reconstruction in large LArTPCs: algorithm description and quantitative evaluation with MicroBooNE simulation

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, space points with associated are reconstructed prior to the pattern recognition stage. Pattern techniques, including track trajectory d Q /d x (ionization per unit length) fitting, neutrino vertex shower separation, particle-level clustering, particle identification then applied on these as well original 2D measurements. A deep neural network developed enhance of interaction vertex. Compared traditional algorithms, boosts efficiency by relative 30% charged-current ? e interactions. This achieves 80–90% efficiencies primary leptons, after 65.8% (72.9%) (? ? ) Based resulting particles their kinematics, we also achieve 15-20% energy resolutions