Dr Patrick Green

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.

Abstract:

Obtaining a high-quality interaction model with associated uncertainties is essential for neutrino experiments studying oscillations, nuclear scattering processes, or both. As a primary input to the MicroBooNE experiment’s next generation of neutrino cross section measurements and its flagship investigation of the MiniBooNE low-energy excess, we present a new tune of the charged-current pionless (CC0π) interaction cross section via the two major contributing processes—charged-current quasielastic and multinucleon interaction models—within version 3.0.6 of the GENIE neutrino event generator. Parameters in these models are tuned to muon neutrino CC0π cross section data obtained by the T2K experiment, which provides an independent set of neutrino interactions with a neutrino flux in a similar energy range to MicroBooNE’s neutrino beam. Although the fit is to muon neutrino data, the information carries over to electron neutrino simulation because the same underlying models are used in GENIE. A number of novel fit parameters were developed for this work, and the optimal parameters were chosen from existing and new sets. We choose to fit four parameters that have not previously been constrained by theory or data. Thus, this will be called a theory-driven tune. The result is an improved match to the T2K CC0π data with more well-motivated uncertainties based on the fit.