论文标题
使用机器学习技术的大型液体闪烁体检测器的能源重建：聚合特征方法
Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach
论文作者
论文摘要
由液体闪烁体目标组成的大规模探测器被一系列照片 - 多层型管（PMT）包围，广泛用于现代中微子实验中：Borexino，Kamland，Daya Bay，Double Chooz，Reno，Reno，Reno和即将到来的Juno以及其卫星检测器检测器Tao。这样的设备能够测量可以从PMT通道上的光及其空间和时间分布得出的中微子能量。但是，在大规模探测器中实现良好的能量解决方案是具有挑战性的。在这项工作中，我们介绍了朱诺探测器中最先进的朱诺探测器中能量重建的机器学习方法。我们专注于0-10 MeV的能量范围的正电子事件，该事件与Juno中的主要信号相对应 - 中微子源自核反应堆核心，并通过逆β衰变通道检测到。我们考虑以下模型：增强决策树和完全连接的深神经网络，并使用PMT收集的信息进行了综合特征训练。我们描述了我们功能工程程序的详细信息，并表明机器学习模型可以使用工程功能的子集提供能源分辨率$σ= 3 \％$。用于模型培训和测试的数据集由Monte Carlo方法与官方Juno软件生成。
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.
