论文标题
部分可观测时空混沌系统的无模型预测
Reconstruction of the event vertex in the PandaX-III experiment with convolution neural network
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 tracks left by charged particles in a gaseous time projection chamber~(TPC) incorporate important information about the interaction process and drift of electrons in gas. The electron diffusion information carried by the tracks is an effective signature to reconstruct $z_0$, the vertex position in drift direction at which the event takes place. In this paper, we propose to reconstruct $z_0$ with convolution neural network~(CNN) in the PandaX-III experiment. A CNN model VGGZ0net is built and validated with Monte Carlo simulation data. It gives $z_0$ with a 11~cm precision for the events above 2~MeV uniformly distributed along a drift distance of 120~cm, and then the electron lifetime can be deduced. The energy resolution of detector is significantly improved after the electron lifetime correction, i.e., from 10.1\% to 4.0\% FWHM at the Q-value of double beta decay of $^{136}$Xe for the scenario with an electron lifetime of 6.5~ms. The CNN model is also successfully applied to the experimental data of the PandaX-III prototype detector for $z_0$ reconstruction.
