In:
Journal of Instrumentation, IOP Publishing, Vol. 17, No. 01 ( 2022-01-01), p. P01037-
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 d Q /d x (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 ν e interactions. This
pattern recognition achieves 80–90% reconstruction efficiencies for primary leptons, after a 65.8% (72.9%) vertex efficiency for
charged-current ν e (ν μ ) interactions. Based on the
resulting reconstructed particles and their kinematics, we also achieve 15-20% energy reconstruction resolutions for
charged-current neutrino interactions.
Type of Medium:
Online Resource
ISSN:
1748-0221
DOI:
10.1088/1748-0221/17/01/P01037
Language:
Unknown
Publisher:
IOP Publishing
Publication Date:
2022
detail.hit.zdb_id:
2235672-1
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