Search for an anomalous excess of charged-current quasielastic nu(e) interactions with the MicroBooNE experiment using Deep-Learning-based reconstruction

We present a measurement of the nu(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 (1e1p). Multiple novel techniques are employed to identify a 1e1p 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 nu(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 nu(mu) CCQE interactions as a constraint. We use a simplified model to translate the MiniBooNE LEE observation into a prediction for a nu(e) signal in MicroBooNE. A Delta chi(2) test statistic, based on the combined Neyman-Pearson chi(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 sigma) confidence level, while the MicroBooNE data yield an exclusion of 0.25 (0.38) times the median MiniBooNE LEE signal strength at 90% (2 sigma) confidence level.