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The use of Boosted Decision Trees for Energy Reconstruction in JUNO experiment

Arsenii Gavrikov · CERN

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About

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 17 612 + 25 600 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)..

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The talk

  1. The use of Boosted Decision Trees for Energy Reconstruction in JUNO experiment 2021-05-18 · 15 min · CERN's player

Credits and licence

Institution
CERN
Department
Conferences
Speaker
Arsenii Gavrikov
Year
2021
Language
English
Subjects
Physics, Computer science, Earth and environment
Source
CERN
Licence
CERN, as CERN states it. CERN's terms
Attribution
© CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
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