论文标题
机器学习增强了相关光谱实验中量化非平衡动力学的算法，以达到框架率限制的时间分辨率
Machine Learning Enhances Algorithms for Quantifying Non-Equilibrium Dynamics in Correlation Spectroscopy Experiments to Reach Frame-Rate-Limited Time Resolution
论文作者
论文摘要
分析非平衡动力学的X射线光子相关光谱（XPC）数据通常需要对强度强度相关函数的年龄区域进行手动构造。这导致时间分辨率的损失和量化动力学的参数的系统误差的积累，尤其是在噪声相当大的情况下。此外，具有高数据收集率的实验使需要自动化的在线分析，在这种情况下是不可能的。在这里，我们将脱氧自动编码器模型集成到算法中，以分析非平衡性两次强度强度相关函数。该模型可以应用于任意大小的输入。降低噪声可以提取仅按帧速率限制时间分辨率来表征样本动力学的参数。它不仅可以改善数据的定量用法，而且还可以自动化分析工作流程。讨论了用于不确定性定量和扩展的各种方法的异常检测方法。
Analysis of X-ray Photon Correlation Spectroscopy (XPCS) data for non-equilibrium dynamics often requires manual binning of age regions of an intensity-intensity correlation function. This leads to a loss of temporal resolution and accumulation of systematic error for the parameters quantifying the dynamics, especially in cases with considerable noise. Moreover, the experiments with high data collection rates create the need for automated online analysis, where manual binning is not possible. Here, we integrate a denoising autoencoder model into algorithms for analysis of non-equilibrium two-time intensity-intensity correlation functions. The model can be applied to an input of an arbitrary size. Noise reduction allows to extract the parameters that characterize the sample dynamics with temporal resolution limited only by frame rates. Not only does it improve the quantitative usage of the data, but it also creates the potential for automating the analytical workflow. Various approaches for uncertainty quantification and extension of the model for anomalies detection are discussed.
