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arXiv 2206.09040physics.ins-detcs.LG
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基于机器学习技术的大型液体scintillator探测器能量重构:聚集特征方法

Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach

HSE University(高等经济大学);Joint Institute for Nuclear Research(联合核研究所)

Arsenii Gavrikov, Yury Malyshkin, Fedor Ratnikov

摘要:

由光电倍增管(PMT)阵列包围的液体闪烁体靶组成的大规模探测器广泛用于现代中微子实验:Borexino,KamLAND,大亚湾,Double Bronze,RENO和即将推出的JUNO及其卫星探测器TAO。这样的装置能够测量中微子能量,其可以从光的量及其在PMT通道上的空间和时间分布导出。然而,在大规模探测器中实现精细的能量分辨率是具有挑战性的。在这项工作中,我们提出了在JUNO探测器中进行能量重建的机器学习方法,这是最先进的探测器。我们集中在0-10 MeV的能量范围内的正电子事件,这对应于JUNO的主要信号-中微子起源于核反应堆堆芯,并通过逆β衰变通道检测。我们考虑以下模型:Boosted Decision Trees和Fully Connected Deep Neural Network,基于聚合特征进行训练,使用PMT收集的信息进行计算。我们描述了我们的特征工程过程的细节,并表明机器学习模型可以提供能量分辨率$σ= 3\%$在1兆电子伏使用工程特征的子集。用于模型训练和测试的数据集是使用官方JUNO软件通过Monte Carlo方法生成的。

英文摘要:

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 $σ= 3\%$ 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.

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