| Issue |
EPJ Web Conf.
Volume 251, 2021
25th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021)
|
|
|---|---|---|
| Article Number | 03014 | |
| Number of page(s) | 9 | |
| Section | Offline Computing | |
| DOI | https://doi.org/10.1051/epjconf/202125103014 | |
| Published online | 23 August 2021 | |
https://doi.org/10.1051/epjconf/202125103014
The use of Boosted Decision Trees for Energy Reconstruction in JUNO experiment
1 HSE University, Moscow, Russia
2 Joint Institute for Nuclear Research, Dubna, Russia
* e-mail: asgavrikov@edu.hse.ru
** e-mail: fedor.ratnikov@cern.ch
Published online: 23 August 2021
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).
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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