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

The MicroBooNE collaboration Abratenko, P. ; An, R. ; Anthony, J. ; et al.
Phys.Rev.D 105 (2022) 112003, 2022.
Inspire Record 1953568 DOI 10.17182/hepdata.114859

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 quasi-elastic (CCQE) events. The topology of such signal events has a final state with 1 electron, 1 proton, and 0 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 $29.0 \pm 1.9_\text{(sys)} \pm 5.4_\text{(stat)}$ 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

7 data tables

Observed NuE data and background (+ LEE) prediction, including the muon neutrino background prediction from the empirical fit, for arXiv:2110.14080. The prediction incorporates the constraint from the 1mu1p sample

Observed NuE data and background (+ LEE) prediction, including the muon neutrino background prediction from the empirical fit, for arXiv:2110.14080. The prediction does not incorporate the constraint from the 1mu1p sample

NuE background fractional covariance matrix after the 1mu1p constraint from arXiv:2110.14080

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