Recently, there has been significant interest in applying machine learning
(ML) techniques to X-ray scattering experiments, which proves to be a valuable
tool for enhancing research that involves large or rapidly generated datasets.
ML allows for the automated interpretation of experimental results,
particularly those obtained from synchrotron or neutron facilities. The speed
at which ML models can process data presents an important opportunity to
establish a closed-loop feedback system, enabling real-time decision-making
based on online data analysis. In this study, we describe the incorporation of
ML into a closed-loop workflow for X-ray reflectometry (XRR), using the growth
of organic thin films as an example. Our focus lies on the beamline integration
of ML-based online data analysis and closed-loop feedback. We present solutions
that provide an elementary data analysis in real time during the experiment
without introducing the additional software dependencies in the beamline
control software environment. Our data demonstrates the accuracy and robustness
of ML methods for analyzing XRR curves and Bragg reflections and its autonomous
control over a vacuum deposition setup.

应用机器学习技术对 X 射线散射实验进行自动解释和实时数据分析，并将机器学习与封闭式回路工作流程相结合，以生长有机薄膜为例，采用 X 射线反射测量进行自动控制。

闭环：基于机器学习的在线数据分析在同步辐射光束线环境中实现的自主实验

Closing the loop: Autonomous experiments enabled by  machine-learning-based online data analysis in synchrotron beamline  environments

X-ray imaging is widely used for non-destructive detection of defects in
industrial products on a conveyor belt. Real-time detection requires highly
accurate, robust, and fast algorithms to analyze X-ray images. Deep
convolutional neural networks (DCNNs) satisfy these requirements if a large
amount of labeled data is available. To overcome the challenge of collecting
these data, different methods of X-ray image generation can be considered.
Depending on the desired level of similarity to real data, various physical
effects either should be simulated or can be ignored. X-ray scattering is known
to be computationally expensive to simulate, and this effect can heavily
influence the accuracy of a generated X-ray image. We propose a methodology for
quantitative evaluation of the effect of scattering on defect detection. This
methodology compares the accuracy of DCNNs trained on different versions of the
same data that include and exclude the scattering signal. We use the
Probability of Detection (POD) curves to find the size of the smallest defect
that can be detected with a DCNN and evaluate how this size is affected by the
choice of training data. We apply the proposed methodology to a model problem
of defect detection in cylinders. Our results show that the exclusion of the
scattering signal from the training data has the largest effect on the smallest
detectable defects. Furthermore, we demonstrate that accurate inspection is
more reliant on high-quality training data for images with a high quantity of
scattering. We discuss how the presented methodology can be used for other
tasks and objects.

通过对 X-ray 扫描中 X-ray 散射算法的定量评估，对比了在包含和不包含散射信号的情况下进行训练后，采用深度卷积神经网络（DCNN）进行 defect detection 的准确性。结果表明，不包含散射信号的训练数据对最小可检测缺陷的大小具有最大影响，更高质量的训练数据可以更好地处理高量散射图像的准确检测。