标题： 从LCLS-II-HE最大化科学产出的端到端机器学习方法 显示英文标题

标题： A Start To End Machine Learning Approach To Maximize Scientific Throughput From The LCLS-II-HE

摘要： 随着光源亮度的提高，包括APS的衍射极限亮度升级和LCLS的高重复率升级，在其中提议的实验正变得越来越复杂。例如，LCLS-II-HE中的实验需要在千米长的电子加速器末端、百米长的波荡器段以及数十米长的X射线光学系统末端，将X射线束直径控制在微米级别内，并且指向稳定性达到纳弧度量级。这种亮度的提升将使数据生产率提高到与世界上最大的数据生成器相媲美的程度。如果没有实时主动反馈控制和优化的数据处理流程，研究人员将被大量无用的数据淹没，无法提取出近期亮度升级所承诺的复杂见解。本文中，我们概述了SLAC正在开发的战略，利用机器学习驱动的方法实现从电子注入器到电子加速器起点，再到多维X射线光学系统，直至LCLS的实验终端站及高读取速率、多兆像素探测器的全自动化和实时知识提取，以向用户提供设计性能。通过加速器、光学和最终用户应用的例子来说明这一点。 显示英文摘要

摘要： With the increasing brightness of Light sources, including the Diffraction-Limited brightness upgrade of APS and the high-repetition-rate upgrade of LCLS, the proposed experiments therein are becoming increasingly complex. For instance, experiments at LCLS-II-HE will require the X-ray beam to be within a fraction of a micron in diameter, with pointing stability of a few nanoradians, at the end of a kilometer-long electron accelerator, a hundred-meter-long undulator section, and tens of meters long X-ray optics. This enhancement of brightness will increase the data production rate to rival the largest data generators in the world. Without real-time active feedback control and an optimized pipeline to transform measurements to scientific information and insights, researchers will drown in a deluge of mostly useless data, and fail to extract the highly sophisticated insights that the recent brightness upgrades promise. In this article, we outline the strategy we are developing at SLAC to implement Machine Learning driven optimization, automation and real-time knowledge extraction from the electron-injector at the start of the electron accelerator, to the multidimensional X-ray optical systems, and till the experimental endstations and the high readout rate, multi-megapixel detectors at LCLS to deliver the design performance to the users. This is illustrated via examples from Accelerator, Optics and End User applications.