https://doi.org/10.1140/epjc/s10052-022-11004-6
Regular Article - Experimental Physics
Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach
1
HSE University, Moscow, Russia
2
Joint Institute for Nuclear Research, Dubna, Russia
3
Dipartimento di Fisica e Astronomia dell’Universitá di Padova and INFN Sezione di Padova, Padua, Italy
Received:
22
June
2022
Accepted:
4
November
2022
Published online:
14
November
2022
Large-scale detectors consisting of a liquid scintillator target surrounded by an array of photo-multiplier tubes (PMTs) are widely used in the modern neutrino experiments: Borexino, KamLAND, Daya Bay, Double Chooz, RENO, and the upcoming JUNO with its satellite detector TAO. Such apparatuses are able to measure neutrino energy which can be derived from the amount of light and its spatial and temporal distribution over PMT channels. However, achieving a fine energy resolution in large-scale detectors is challenging. In this work, we present machine learning methods for energy reconstruction in the JUNO detector, the most advanced of its type. We focus on positron events in the energy range of 0–10 MeV which corresponds to the main signal in JUNO – neutrinos originated from nuclear reactor cores and detected via the inverse beta decay channel. We consider the following models: Boosted Decision Trees and Fully Connected Deep Neural Network, trained on aggregated features, calculated using the information collected by PMTs. We describe the details of our feature engineering procedure and show that machine learning models can provide the energy resolution 
at 1 MeV using subsets of engineered features. The dataset for model training and testing is generated by the Monte Carlo method with the official JUNO software.
© The Author(s) 2022
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Funded by SCOAP3. SCOAP3 supports the goals of the International Year of Basic Sciences for Sustainable Development.
