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

Journal Article · · Journal of Instrumentation
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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. Therefore, 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.

Research Organization:
Brookhaven National Lab. (BNL), Upton, NY (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States); Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Univ. of Michigan, Ann Arbor, MI (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP); USDOE Office of Science (SC), Basic Energy Sciences (BES)
Contributing Organization:
MicroBooNE Collaboration
Grant/Contract Number:
SC0012704; AC02-07CH11359; AC02-76SF00515; SC0023471; SC0007859
OSTI ID:
1838305
Alternate ID(s):
OSTI ID: 1833287; OSTI ID: 1867504; OSTI ID: 1906300; OSTI ID: 1972675
Report Number(s):
BNL-222573-2022-JAAM; FERMILAB-PUB-21-509-ND; arXiv:2110.13961; TRN: US2300977
Journal Information:
Journal of Instrumentation, Vol. 17, Issue 01; ISSN 1748-0221
Publisher:
Institute of Physics (IOP)Copyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
wire-cell
pattern recognition
deep neural network