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, 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 νe interactions. This pattern recognition achieves 80–90% reconstruction efficiencies for primary leptons, after a 65.8% (72.9%) vertex efficiency for charged-current νe (νμ) interactions. Based on the resulting reconstructed particles and their kinematics, we also achieve 15-20% energy reconstruction resolutions for charged-current neutrino interactions.

First Measurement of Energy-Dependent Inclusive Muon Neutrino Charged-Current Cross Sections on Argon with the MicroBooNE Detector

“…In particular, the "generic neutrino detection" [32,33], which limits the cosmic-ray muon backgrounds to below 15% at over 80% ν µ CC selection efficiency, is used as a pre-selection. The ν µ CC event selection is further improved using a set of pattern recognition techniques, including i) neutrino vertex identification, ii) track/shower topology separation, iii) particle identification, and iv) particle flow reconstruction in the Wire-Cell reconstruction package [34,35].…”

“…Three methods are used to reconstruct the energy of tracks and electromagnetic (EM) showers [34,35]: i) The energy of a stopped charged particle track can be estimated by its travel range using the NIST PSTAR database [37]. ii) The kinetic energy of a charged particle track can be estimated by integrating over the reconstructed energy loss per unit length dE/dx, which is calculated from the measured dQ/dx (ionization charge per unit length) using a recombination model [38].…”

“…For stopping tracks with trajectories longer than 4 cm, the range method is used to estimate the energy. For short tracks (<4 cm), tracks exiting the detector, tracks with "wiggled" topology [34] (e.g. low-energy electrons), and muon tracks with identified δ rays, the recombination method is used to estimate its kinetic energy.…”

Physics Opportunities in the ORNL Spallation Neutron Source Second Target Station Era

“…While this method of readout has been successful in several detectors, it suffers from an intrinsic limitation in resolving ambiguities for various topologies. Novel event reconstruction techniques may be employed to overcome these difficulties [46][47][48][49][50] but are often very complex and not readily adaptable to A pixel-based readout scheme had not been previously considered for large LArTPCs because of the increased number of readout channels, and the high data rate and power consumption. A transformative step realized by the LArPix [53] and Q-Pix [54] consortia now allows one to build a fully pixelated low-power charge readout capable of efficiently and accurately capturing signal information.…”

DUNE Offline Computing (Conceptual Design Report)

“…These results finally enable very accurate reconstruction of quantities such locations of interaction vertices, track branch points, and dQ/dx and from there dE/dx along tracks. These algorithms were initially developed for MicroBooNE [77] where they have been found to outperform others [78,79]. There is an ongoing effort to generalize and "port" these algorithms into WCT for use by DUNE and other LArTPC detectors.…”