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We present a measurement of the $\nu_e$-interaction rate in the MicroBooNEdetector that addresses the observed MiniBooNE anomalous low-energy excess(LEE). The approach taken isolates neutrino interactions consistent with thekinematics of charged-current quasi-elastic (CCQE) events. The topology of suchsignal events has a final state with 1 electron, 1 proton, and 0 mesons($1e1p$). Multiple novel techniques are employed to identify a $1e1p$ finalstate, including particle identification that use two methods ofdeep-learning-based image identification, and event isolation using a boosteddecision-tree ensemble trained to recognize two-body scattering kinematics.This analysis selects 25 $\nu_e$-candidate events in the reconstructed neutrinoenergy range of 200--1200\,MeV, while $29.0 \pm 1.9_\text{(sys)} \pm5.4_\text{(stat)}$ are predicted when using $\nu_\mu$ CCQE interactions as aconstraint. We use a simplified model to translate the MiniBooNE LEEobservation 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 LEEsignal strength. Using this technique, in the case of no LEE signal, we expectthis analysis to exclude a normalization factor of 0.75 (0.98) times the medianMiniBooNE LEE signal strength at 90\% ($2\sigma$) confidence level, while theMicroBooNE data yield an exclusion of 0.25 (0.38) times the median MiniBooNELEE signal strength at 90\% ($2\sigma$) confidence

physical review. d/physical review. d.

Search for an anomalous excess of charged-current quasi-elastic $\nu_e$  interactions with the MicroBooNE experiment using Deep-Learning-based  reconstruction