The Deep Underground Neutrino Experiment (DUNE) will be a world-class neutrino observatory and nucleon decay detector designed to answer fundamental questions about the nature of elementary particles and their role in the universe.
We have developed a convolutional neural network that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a ν μ charged-current neutral pion data samples.
We present upper limits on the production of heavy neutral leptons (HNLs) decaying to μπ pairs using data collected with the MicroBooNE liquid-argon time projection chamber (TPC) operating at Fermilab. This search is the first of its kind performed in a liquid-argon TPC. We use data collected in 2017 and 2018 corresponding to an exposure of 2.0 × 10 20 protons on target from the Fermilab Booster Neutrino Beam, which produces mainly muon neutrinos with an average energy of ≈800 MeV. HNLs with higher mass are expected to have a longer time of flight to the liquid-argon TPC than Standard Model neutrinos. The data are therefore recorded with a dedicated trigger configured to detect HNL decays that occur after the neutrino spill reaches the detector. We set upper limits at the 90% confidence level on the element jU μ4 j 2 of the extended PMNS mixing matrix in the range jU μ4 j 2 < ð6.6-0.9Þ × 10 −7 for Dirac HNLs and jU μ4 j 2 < ð4.7-0.7Þ × 10 −7 for Majorana HNLs, assuming HNL masses between 260 and 385 MeV and jU e4 j 2 ¼ jU τ4 j 2 ¼ 0.
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