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.
MicroBooNE is a neutrino experiment located in the Booster Neutrino Beamline (BNB) at Fermilab, which collected data from 2015 to 2021. MicroBooNE’s liquid argon time projection chamber (LArTPC) is ...accompanied by a photon detection system consisting of 32 photomultiplier tubes used to measure the argon scintillation light and determine the timing of neutrino interactions. Analysis techniques combining light signals and reconstructed tracks are applied to achieve a neutrino interaction time resolution of $\mathscr{O}$(1 ns) The result obtained allows MicroBooNE to access the nanosecond beam structure of the BNB for the first time. The timing resolution achieved will enable significant enhancement of cosmic background rejection for all neutrino analyses. Furthermore, the ns timing resolution opens new avenues to search for long-lived-particles such as heavy neutral leptons in MicroBooNE, as well as in future large LArTPC experiments, namely the SBN program and DUNE.
We report on a flux-integrated multi-differential measurement of charged-current muon neutrino scattering on argon with one muon and one proton in the final state using the Booster Neutrino Beam and ...MicroBooNE detector at Fermi National Accelerator Laboratory. The data are studied as a function of various kinematic imbalance variables and of a neutrino energy estimator, and are compared to a number of event generator predictions. We find that the measured cross sections in different phase-space regions are sensitive to nuclear effects. Our results provide precision data to test and improve the neutrino-nucleus interaction models needed to perform high-accuracy oscillation analyses. Specific regions of phase-space are identified where further model refinements are most needed.
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