Testing the data framework for an AI algorithm in preparation for high data rate X-ray facilities
Abstract
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.
Keywords
Cite
@article{arxiv.2210.10137,
title = {Testing the data framework for an AI algorithm in preparation for high data rate X-ray facilities},
author = {Hongwei Chen and Sathya R. Chitturi and Rajan Plumley and Lingjia Shen and Nathan C. Drucker and Nicolas Burdet and Cheng Peng and Sougata Mardanya and Daniel Ratner and Aashwin Mishra and Chun Hong Yoon and Sanghoon Song and Matthieu Chollet and Gilberto Fabbris and Mike Dunne and Silke Nelson and Mingda Li and Aaron Lindenberg and Chunjing Jia and Youssef Nashed and Arun Bansil and Sugata Chowdhury and Adrian E. Feiguin and Joshua J. Turner and Jana B. Thayer},
journal= {arXiv preprint arXiv:2210.10137},
year = {2023}
}