Normal mode analysis of a relaxation process with Bayesian inference
Normal mode analysis of a relaxation process with Bayesian inference
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使用贝叶斯推理对松弛过程进行正则模式分析
DOI：
10.1080/14686996.2020.1713703
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发表时间：
2020
影响因子：
5.5
通讯作者：
Akai Ichiro,Okada Masato
中科院分区：
文献类型：
--
作者：
Sakata Itsushi;Nagano Yoshihiro;Igarashi Yasuhiko;Murata Shin;Mizoguchi Kohji;Akai Ichiro,Okada Masato
弛豫过程的测量在包括非线性光学在内的许多领域都是必不可少的。弛豫过程提供了许多关于原子/分子结构、化学反应动力学和机理的见解。对于这些过程的分析，提取特定于感兴趣现象的模态（正常模态）是不可避免的。本文从贝叶斯推理的模型选择角度出发，提出了一个系统地提取正态模态的框架。我们的方法包括一种众所周知的称为稀疏促进动态模式分解的方法，该方法分解了阻尼振荡的混合物，以及贝叶斯模型选择框架。我们用铋多晶的相干声子信号和虚拟数据作为弛豫过程的典型例子，数值验证了我们提出的方法的性能。该方法即使在强背景的实验数据中也能成功地提取出正态模态。此外，所选择的模态集对观测噪声具有较强的鲁棒性，该方法可以估计观测噪声的水平。从这些观察结果来看，我们的方法适用于正态分析，特别是对于具有强背景的数据。
Measurements of relaxation processes are essential in many fields, including nonlinear optics. Relaxation processes provide many insights into atomic/molecular structures and the kinetics and mechanisms of chemical reactions. For the analysis of these processes, the extraction of modes that are specific to the phenomenon of interest (normal modes) is unavoidable. In this study we propose a framework to systematically extract normal modes from the viewpoint of model selection with Bayesian inference. Our approach consists of a well-known method called sparsity-promoting dynamic mode decomposition, which decomposes a mixture of damped oscillations, and the Bayesian model selection framework. We numerically verify the performance of our proposed method by using coherent phonon signals of a bismuth polycrystal and virtual data as typical examples of relaxation processes. Our method succeeds in extracting the normal modes even from experimental data with strong backgrounds. Moreover, the selected set of modes is robust to observation noise, and our method can estimate the level of observation noise. From these observations, our method is applicable to normal mode analysis, especially for data with strong backgrounds.