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Item type:Publication, Use of neutrino scattering events with low hadronic recoil to inform neutrino flux and detector energy scale(IOP Publishing Ltd, 2021-08-01)Charged-current neutrino interactions with low hadronic recoil ("low-ν") have a cross-section that is approximately constant versus neutrino energy. These interactions have been used to measure the shape of neutrino fluxes as a function of neutrino energy at accelerator-based neutrino experiments such as CCFR, NuTeV, MINOS and MINERνA. In this paper, we demonstrate that low-ν events can be used to measure parameters of neutrino flux and detector models and that utilization of event distributions over the upstream detector face can discriminate among parameters that affect the neutrino flux model. From fitting a large sample of low-ν events obtained by exposing MINERνA to the NuMI medium-energy beam, we find that the best-fit flux parameters are within their a priori uncertainties, but the energy scale of muons reconstructed in the MINOS detector is shifted by 3.6% (or 1.8 times the a priori uncertainty on that parameter). These fit results are now used in all MINERνA cross-section measurements, and this technique can be applied by other experiments operating at MINERνA energies, such as DUNE.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Neutral pion reconstruction using machine learning in the MINERvA experiment at 〈Ev〉 ∼ 6 GeV(IOP Publishing Ltd, 2021-07-01)This paper presents a novel neutral-pion reconstruction that takes advantage of the machine learning technique of semantic segmentation using MINERvA data collected between 2013–2017, with an average neutrino energy of 6 GeV. Semantic segmentation improves the purity of neutral pion reconstruction from two γs from 70.7 ± 0.9% to 89.3 ± 0.7% and improves the efficiency of the reconstruction by approximately 40%. We demonstrate our method in a charged current neutral pion production analysis where a single neutral pion is reconstructed. This technique is applicable to modern tracking calorimeters, such as the new generation of liquid-argon time projection chambers, exposed to neutrino beams with 〈 E ν 〉 between 1–10 GeV. In such experiments it can facilitate the identification of ionization hits which are associated with electromagnetic showers, thereby enabling improved reconstruction of charged-current ν e events arising from ν μ → ν e appearance.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Vertex finding in neutrino-nucleus interaction: a model architecture comparison(Institute of Physics, 2022-08-01)We compare different neural network architectures for machine learning algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The architectures developed and optimized by hand are compared with the architectures developed in an automated way using the package “Multi-node Evolutionary Neural Networks for Deep Learning” (MENNDL), developed at Oak Ridge National Laboratory. While the domain-expert hand-tuned network was the best performer, the differences were negligible and the auto-generated networks performed as well. There is always a trade-off between human, and computer resources for network optimization and this work suggests that automated optimization, assuming resources are available, provides a compelling way to save significant expert time.