Search for an anomalous excess of charged-current quasielastic <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mi>ν</mml:mi><mml:mi>e</mml:mi></mml:msub></mml:math> interactions with the MicroBooNE experiment using Deep-Learning-based reconstruction

We present a measurement of the 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 e -candidate events in the reconstructed neutrino energy range of 200-1200 MeV, while 29.0 AE 1.9 sys AE 5.4 stat are predicted when using CCQE interactions as a constraint. We use a simplified model to translate the MiniBooNE LEE observation into a prediction for a e signal in MicroBooNE. A 2 test statistic, based on the combined Neyman-Pearson 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) confidence level, while the MicroBooNE data yield an exclusion of 0.25 (0.38) times the median MiniBooNE LEE signal strength at 90% (2) confidence level.