We report on the first measurement of flux-integrated single differential cross sections for charged-current (CC) muon neutrino (νμ) scattering on argon with a muon and a proton in the final state, ...Ar40 (νμ,μp)X. The measurement was carried out using the Booster Neutrino Beam at Fermi National Accelerator Laboratory and the MicroBooNE liquid argon time projection chamber detector with an exposure of 4.59×1019 protons on target. Events are selected to enhance the contribution of CC quasielastic (CCQE) interactions. The data are reported in terms of a total cross section as well as single differential cross sections in final state muon and proton kinematics. We measure the integrated per-nucleus CCQE-like cross section (i.e., for interactions leading to a muon, one proton, and no pions above detection threshold) of (4.93±0.76stat±1.29sys)×10−38 cm2, in good agreement with theoretical calculations. The single differential cross sections are also in overall good agreement with theoretical predictions, except at very forward muon scattering angles that correspond to low-momentum-transfer events.
We present an analysis of MicroBooNE data with a signature of one muon, no pions, and at least one proton above a momentum threshold of 300 MeV/c(CC0πNp). This is the first differential cross-section ...measurement of this topology in neutrino-argon interactions. We achieve a significantly lower proton momentum threshold than previous carbon and scintillator-based experiments. Using data collected from a total of approximately 1.6 × 1020 protons on target, we measure the muon neutrino cross section for the CC0πNp interaction channel in argon at MicroBooNE in the Booster Neutrino Beam which has a mean energy of around 800 MeV. We present the results from a data sample with estimated efficiency of 29% and purity of 76% as differential cross sections in five reconstructed variables: the muon momentum and polar angle, the leading proton momentum and polar angle, and the muon-proton opening angle. We include smearing matrices that can be used to "forward fold" theoretical predictions for comparison with these data. We compare the measured differential cross sections to a number of recent theory predictions demonstrating largely good agreement with this first-ever dataset on argon.
We present upper limits on the production of heavy neutral leptons (HNLs) decaying to μ π 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 × 1020 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 |Uμ4|2 of the extended PMNS mixing matrix in the range |Uμ4|2 < (6.6–0.9) × 10−7 for Dirac HNLs and |Uμ4|2 < (4.7–0.7) × 10−7 for Majorana HNLs, assuming HNL masses between 260 and 385 MeV and |Ue4|2 = |Uτ4|2 = 0.
We present the performance of a semantic segmentation network, sparsessnet, that provides pixel-level classification of MicroBooNE data. The MicroBooNE experiment employs a liquid argon time ...projection chamber for the study of neutrino properties and interactions. sparsessnet is a submanifold sparse convolutional neural network, which provides the initial machine learning based algorithm utilized in one of MicroBooNEs νe-appearance oscillation analyses. The network is trained to categorize pixels into five classes, which are reclassified into two classes more relevant to the current analysis. The output of sparsessnet is a key input in further analysis steps. This technique, used for the first time in liquid argon time projection chambers data and is an improvement compared to a previously used convolutional neural network, both in accuracy and computing resource utilization. The accuracy achieved on the test sample is ≥ 99 %. For full neutrino interaction simulations, the time for processing one image is ≈ 0.5 sec , the memory usage is at 1 GB level, which allows utilization of most typical CPU worker machine.
We present the multiple particle identification (MPID) network, a convolutional neural network for multiple object classification, developed by MicroBooNE. MPID provides the probabilities that an ...interaction includes an e−, γ , μ−, π±, and protons in a liquid argon time projection chamber single readout plane. The network extends the single particle identification network previously developed by MicroBooNE Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber, R. Acciarri et al. J. Instrum. 12, P03011 (2017). MPID takes as input an image either cropped around a reconstructed interaction vertex or containing only activity connected to a reconstructed vertex, therefore relieving the tool from inefficiencies in vertex finding and particle clustering. The network serves as an important component in MicroBooNE's deep-learning-based ν e search analysis. In this paper, we present the network's design, training, and performance on simulation and data from the MicroBooNE detector.
We present a measurement of the combined νe + νe flux-averaged charged-current inclusive cross section on argon using data from the MicroBooNE liquid argon time projection chamber (lartpc) at ...Fermilab. Using the off-axis flux from the NuMI beam, MicroBooNE has reconstructed 214 candidate νe + νe interactions with an estimated exposure of 2.4 × 1020 protons on target. Given the estimated purity of 38.6%, this implies the observation of 80 νe + νe events in argon, the largest such sample to date. The analysis includes the first demonstration of a fully automated application of a dE/dx-based particle discrimination technique of electron- and photon-induced showers in a lartpc neutrino detector. The main background for this first ν e analysis is cosmic ray contamination. Significantly higher purity is expected in underground detectors, as well as with next-generation reconstruction algorithms. We measure the νe + νe flux-averaged charged-current total cross section to be 6.84 ± 1.51 ( stat ) ± 2.33 ( sys ) × 10−39 cm2 / nucleon, for neutrino energies above 250 MeV and an average neutrino flux energy of 905 MeV when this threshold is applied. The measurement is sensitive to neutrino events where the final state electron momentum is above 48 MeV / c , includes the entire angular phase space of the electron, and is in agreement with the theoretical predictions from genie and nuwro. This measurement is also the first demonstration of electron-neutrino reconstruction in a surface lartpc in the presence of cosmic-ray backgrounds, which will be a crucial task for surface experiments like those that comprise the short-baseline neutrino program at Fermilab.
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