| Autor: |
Chen, Hongwei, Chitturi, Sathya R., Plumley, Rajan, Shen, Lingjia, Drucker, Nathan C., Burdet, Nicolas, Peng, Cheng, Mardanya, Sougata, Ratner, Daniel, Mishra, Aashwin, Yoon, Chun Hong, Song, Sanghoon, Chollet, Matthieu, Fabbris, Gilberto, Dunne, Mike, Nelson, Silke, Li, Mingda, Lindenberg, Aaron, Jia, Chunjing, Nashed, Youssef, Bansil, Arun, Chowdhury, Sugata, Feiguin, Adrian E., Turner, Joshua J., Thayer, Jana B. |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
2022 4th Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing (XLOOP) (2022) 1-9 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/XLOOP56614.2022.00006 |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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