论文标题
测试AI算法的数据框架，以准备高数据速率X射线设施
Testing the data framework for an AI algorithm in preparation for high data rate X-ray facilities
论文作者
论文摘要
下一代X射线无电子激光器的出现将能够以连续接近1 MHz的重复速率传递X射线。这将需要开发数据系统来处理这类设施的实验，尤其是对于高吞吐量应用，例如飞秒X射线晶体学和X射线光子波动光谱。在这里，我们演示了一个框架，该框架在LCLS上捕获了单个Shot X射线数据，并实现了机器学习算法以自动从收集的数据中提取对比参数。我们衡量返回结果所需的时间并评估以高数据量使用此框架的可行性。我们使用此实验来以MHz的重复速率确定解决方案对“实时”数据分析的可行性。
The advent of next-generation X-ray free electron lasers will be capable of delivering X-rays at a repetition rate approaching 1 MHz continuously. This will require the development of data systems to handle experiments at these type of facilities, especially for high throughput applications, such as femtosecond X-ray crystallography and X-ray photon fluctuation spectroscopy. Here, we demonstrate a framework which captures single shot X-ray data at the LCLS and implements a machine-learning algorithm to automatically extract the contrast parameter from the collected data. We measure the time required to return the results and assess the feasibility of using this framework at high data volume. We use this experiment to determine the feasibility of solutions for `live' data analysis at the MHz repetition rate.
