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### Electronic data

• Wire_Cell_Pattern_Recognition_2.0.0

Rights statement: This is an author-created, un-copyedited version of an article accepted for publication/published in Journal of Instrumentation. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at doi:10.1088/1748-0221/17/01/P01037

Accepted author manuscript, 3.4 MB, PDF document

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

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published
Article number PO1037 31/01/2022 Journal of Instrumentation 1 17 Published 27/01/22 English

### 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.

### Bibliographic note

This is an author-created, un-copyedited version of an article accepted for publication/published in Journal of Instrumentation. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at doi:10.1088/1748-0221/17/01/P01037