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Creators/Authors contains: "Peters, L."

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  1. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review D)
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  2. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review D)
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  3. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review D)
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  4. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review Letters)
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  5. ; ; ; ; ; ; ; ; ; ; et al ( , The European Physical Journal C)
    Abstract Primary challenges for current and future precision neutrino experiments using liquid argon time projection chambers (LArTPCs) include understanding detector effects and quantifying the associated systematic uncertainties. This paper presents a novel technique for assessing and propagating LArTPC detector-related systematic uncertainties. The technique makes modifications to simulation waveforms based on a parameterization of observed differences in ionization signals from the TPC between data and simulation, while remaining insensitive to the details of the detector model. The modifications are then used to quantify the systematic differences in low- and high-level reconstructed quantities. This approach could be applied to future LArTPC detectors,more »such as those used in SBN and DUNE.« less
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  6. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review Letters)
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  7. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review D)
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  8. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review D)
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  9. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review Letters)
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  10. ; ; ; ; ; ; ; ; ; ; et al ( , Journal of Instrumentation)
    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.more »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.« less
    Free, publicly-accessible full text available January 1, 2023