论文标题
改善具有多种类型的PMT的大型液体闪烁体检测器的基于机器学习的顶点重建
Improving the machine learning based vertex reconstruction for large liquid scintillator detectors with multiple types of PMTs
论文作者
论文摘要
精确的顶点重建对于大型液体闪烁体检测器至关重要。基于机器学习的一种新颖方法已成功地开发了以前在Juno中重建事件顶点的方法。在本文中，通过优化神经网络的输入图像，进一步改善了基于机器学习的顶点重建的性能。通过将不同类型的PMT的信息分开，并添加了PMT的第二次命中的信息，在1 MEV时，顶点分辨率分别提高了约9.4％，在11 MEV时分别提高了9.8％。
Precise vertex reconstruction is essential for large liquid scintillator detectors. A novel method based on machine learning has been successfully developed to reconstruct the event vertex in JUNO previously. In this paper, the performance of machine learning based vertex reconstruction is further improved by optimizing the input images of the neural networks. By separating the information of different types of PMTs as well as adding the information of the second hit of PMTs, the vertex resolution is improved by about 9.4 % at 1 MeV and 9.8 % at 11 MeV, respectively.
