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 νμ charged-current neutral pion data samples.
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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.
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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.
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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.
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We present a search for eV-scale sterile neutrino oscillations in the MicroBooNE liquid argon detector, simultaneously considering all possible appearance and disappearance effects within the 3+1 ...active-to-sterile neutrino oscillation framework. We analyze the neutrino candidate events for the recent measurements of charged-current ν_{e} and ν_{μ} interactions in the MicroBooNE detector, using data corresponding to an exposure of 6.37×10^{20} protons on target from the Fermilab booster neutrino beam. We observe no evidence of light sterile neutrino oscillations and derive exclusion contours at the 95% confidence level in the plane of the mass-squared splitting Δm_{41}^{2} and the sterile neutrino mixing angles θ_{μe} and θ_{ee}, excluding part of the parameter space allowed by experimental anomalies. Cancellation of ν_{e} appearance and ν_{e} disappearance effects due to the full 3+1 treatment of the analysis leads to a degeneracy when determining the oscillation parameters, which is discussed in this Letter and will be addressed by future analyses.
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We report a measurement of the energy-dependent total charged-current cross section σ(E_{ν}) for inclusive muon neutrinos scattering on argon, as well as measurements of flux-averaged differential ...cross sections as a function of muon energy and hadronic energy transfer (ν). Data corresponding to 5.3×10^{19} protons on target of exposure were collected using the MicroBooNE liquid argon time projection chamber located in the Fermilab booster neutrino beam with a mean neutrino energy of approximately 0.8 GeV. The mapping between the true neutrino energy E_{ν} and reconstructed neutrino energy E_{ν}^{rec} and between the energy transfer ν and reconstructed hadronic energy E_{had}^{rec} are validated by comparing the data and Monte Carlo (MC) predictions. In particular, the modeling of the missing hadronic energy and its associated uncertainties are verified by a new method that compares the E_{had}^{rec} distributions between data and a MC prediction after constraining the reconstructed muon kinematic distributions, energy, and polar angle to those of data. The success of this validation gives confidence that the missing energy in the MicroBooNE detector is well modeled and underpins first-time measurements of both the total cross section σ(E_{ν}) and the differential cross section dσ/dν on argon.
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We report the first measurement of the flux-integrated cross section of νμ charged-current single π0 production on argon. This measurement is performed with the MicroBooNE detector, an 85 ton active ...mass liquid argon time projection chamber exposed to the Booster Neutrino Beam at Fermilab. This result on argon is compared to past measurements on lighter nuclei to investigate the scaling assumptions used in models of the production and transport of pions in neutrino-nucleus scattering. The techniques used are an important demonstration of the successful reconstruction and analysis of neutrino interactions producing electromagnetic final states using a liquid argon time projection chamber operating at the Earth's surface.
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We report on the first measurement of flux-integrated single differential cross sections for charged-current (CC) muon neutrino ($\nu_{\mu}$) scattering on argon with a muon and a proton in the final ...state, 40Ar $(\nu_{\mu},μp)X$. 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.
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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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