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Wire-Cell is a 3D event reconstruction package for liquid argon timeprojection chambers. Through geometry, time, and drifted charge from multiplereadout wire planes, 3D space points with associated charge are reconstructedprior 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 onthese 3D space points as well as the original 2D projection measurements. Adeep neural network is developed to enhance the reconstruction of the neutrinointeraction vertex. Compared to traditional algorithms, the deep neural networkboosts the vertex efficiency by a relative 30\% for charged-current $\nu_e$interactions. This pattern recognition achieves 80-90\% reconstructionefficiencies for primary leptons, after a 65.8\% (72.9\%) vertex efficiency forcharged-current $\nu_e$ ($\nu_\mu$) interactions. Based on the resultingreconstructed particles and their kinematics, we also achieve 15-20\% energyreconstruction resolutions for charged-current neutrino interactions.

journal of instrumentation

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