  We present a deep learning-based method for estimating the neutrino energy of
charged-current neutrino-argon interactions. We employ a recurrent neural
network (RNN) architecture for neutrino energy estimation in the MicroBooNE
experiment, utilizing liquid argon time projection chamber (LArTPC) detector
technology. Traditional energy estimation approaches in LArTPCs, which largely
rely on reconstructing and summing visible energies, often experience sizable
biases and resolution smearing because of the complex nature of neutrino
interactions and the detector response. The estimation of neutrino energy can
be improved after considering the kinematics information of reconstructed
final-state particles. Utilizing kinematic information of reconstructed
particles, the deep learning-based approach shows improved resolution and
reduced bias for the muon neutrino Monte Carlo simulation sample compared to
the traditional approach. In order to address the common concern about the
effectiveness of this method on experimental data, the RNN-based energy
estimator is further examined and validated with dedicated data-simulation
consistency tests using MicroBooNE data. We also assess its potential impact on
a neutrino oscillation study after accounting for all statistical and
systematic uncertainties and show that it enhances physics sensitivity. This
method has good potential to improve the performance of other physics analyses.
