Dr Patrick Green

Abstract:

This article presents a measurement of νe interactions without pions in the final state using the MicroBooNE experiment and an investigation into the excess of low-energy electromagnetic events observed by the MiniBooNE collaboration. The measurement is performed in exclusive channels with (1eNp0π) and without (1e0p0π) visible final-state protons using 6.86×1020 protons on target of data collected from the Booster Neutrino Beam at Fermilab. Events are reconstructed with the Pandora pattern recognition toolkit and selected using additional topological information from the MicroBooNE liquid argon time projection chamber. Using a goodness-of-fit test the data are found to be consistent with the predicted number of events with nominal flux and interaction models with a p-value of 0.098 in the two channels combined. A model based on the low-energy excess observed in MiniBooNE is introduced to quantify the strength of a possible νe excess. The analysis suggests that, if an excess is present, it is not consistent with a scaling of the νe contribution to the flux as predicted by the signal model used in the analysis. Combined, the 1eNp0π and 1e0p0π channels do not give a conclusive indication about the tested model, but separately they both disfavor the lowenergy excess model at >90% CL. The observation in the most sensitive 1eNp0π channel is below the prediction and consistent with no excess. In the less sensitive 1e0p0π channel the observation at low energy is above the prediction, while overall there is agreement over the full energy spectrum.

Abstract:

We present a measurement of the ν e -interaction rate in the MicroBooNE detector that addresses the observed MiniBooNE anomalous low-energy excess (LEE). The approach taken isolates neutrino interactions consistent with the kinematics of charged-current quasielastic (CCQE) events. The topology of such signal events has a final state with one electron, one proton, and zero mesons ( 1 e 1 p ). Multiple novel techniques are employed to identify a 1 e 1 p final state, including particle identification that use two methods of Deep-Learning-based image identification and event isolation using a boosted decision-tree ensemble trained to recognize two-body scattering kinematics. This analysis selects 25 ν e -candidate events in the reconstructed neutrino energy range of 200–1200 MeV, while 29.0 ± 1. 9 ( sys ) ± 5. 4 ( stat ) are predicted when using ν μ CCQE interactions as a constraint. We use a simplified model to translate the MiniBooNE LEE observation into a prediction for a ν e signal in MicroBooNE. A Δ χ 2 test statistic, based on the combined Neyman–Pearson χ 2 formalism, is used to define frequentist confidence intervals for the LEE signal strength. Using this technique, in the case of no LEE signal, we expect this analysis to exclude a normalization factor of 0.75 (0.98) times the median MiniBooNE LEE signal strength at 90% ( 2 σ ) confidence level, while the MicroBooNE data yield an exclusion of 0.25 (0.38) times the median MiniBooNE LEE signal strength at 90% ( 2 σ ) confidence level.

Abstract:

We present a measurement of νe interactions from the Fermilab Booster Neutrino Beam using the MicroBooNE liquid argon time projection chamber to address the nature of the excess of low energy interactions observed by the MiniBooNE Collaboration. Three independent νe searches are performed across multiple single electron final states, including an exclusive search for two-body scattering events with a single proton, a semi-inclusive search for pionless events, and a fully inclusive search for events containing all hadronic final states. With differing signal topologies, statistics, backgrounds, reconstruction algorithms, and analysis approaches, the results are found to be either consistent with or modestly lower than the nominal νe rate expectations from the Booster Neutrino Beam and no excess of νe events is observed.

Abstract:

Primary challenges for current and future precision neutrino experiments using liquid argon time projection chambers (LArTPCs) include understanding detector effects and quantifying the associated systematic uncertainties. This paper presents a novel technique for assessing and propagating LArTPC detector-related systematic uncertainties. The technique makes modifications to simulation waveforms based on a parameterization of observed differences in ionization signals from the TPC between data and simulation, while remaining insensitive to the details of the detector model. The modifications are then used to quantify the systematic differences in low- and high-level reconstructed quantities. This approach could be applied to future LArTPC detectors, such as those used in SBN and DUNE.