  The Jiangmen Underground Neutrino Observatory (JUNO) is a neutrino experiment
with a broad physical program. The main goals of JUNO are the determination of
the neutrino mass ordering and high precision investigation of neutrino
oscillation properties. The precise reconstruction of the event energy is
crucial for the success of the experiment.
  JUNO is equiped with 17612 + 25600 PMT channels of two kind which provide
both charge and hit time information. In this work we present a fast Boosted
Decision Trees model using small set of aggregated features. The model predicts
event energy deposition. We describe the motivation and the details of our
feature engineering and feature selection procedures. We demonstrate that the
proposed aggregated approach can achieve a reconstruction quality that is
competitive with the quality of much more complex models like Convolution
Neural Networks (ResNet, VGG and GNN).
