论文标题
在朱诺中央探测器中重建MUON捆绑包
Reconstruction of Muon Bundle in the JUNO Central Detector
论文作者
论文摘要
储层计算是预测湍流的有力工具,其简单的架构具有处理大型系统的计算效率。然而,其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间,然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用,该系统模拟了湍流的若干特征。我们表明,使用径向基函数作为非线性投影器,即使只有部分观测并且不知道控制方程,也能稳健地捕捉复杂的系统非线性。最后,我们表明,当测量稀疏、不完整且带有噪声,甚至控制方程变得不准确时,我们的网络仍然可以产生相当准确的预测,从而为实际湍流系统的无模型预测铺平了道路。
The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose neutrino experiment. One of the main goals is to determine the neutrino mass ordering by precisely measuring the energy spectrum of reactor antineutrinos. For reactor antineutrino detection, cosmogenic backgrounds such as $^9$Li/$^8$He and fast neutrons induced by cosmic muons should be rejected carefully by applying muon veto cuts, which requires good muon track reconstruction. With a 20~kton liquid scintillator detector, simulation shows the proportion of muon bundles to be around 8\% in the JUNO, while its reconstruction is rarely discussed in previous experiments. According to the charge response of the PMT array, this paper proposes an efficient algorithm for muon bundle track reconstruction. This is the first reconstruction of muon bundles in a large volume liquid scintillator detector. Additionally, the algorithm shows good performance and potential in reconstruction for both single muon and double muons. The spatial resolution of single muon reconstruction is 20~cm and the angular resolution is $0.5^\circ$. As for double muon reconstruction, the spatial resolution and angular resolution could be 30~cm and $1.0^\circ$, respectively. Moreover, this paper has also discussed muon classification and veto strategy.