1. Spectral-brightness optimization of an X-ray free-electron laser by machine-learning-based tuning
- Author
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Eito Iwai, Ichiro Inoue, Hirokazu Maesaka, Takahiro Inagaki, Makina Yabashi, Toru Hara, and Hitoshi Tanaka
- Subjects
x-ray free-electron lasers ,machine learning ,beam tuning ,sacla ,spectral-brightness optimization ,single-shot inline spectrometers ,Nuclear and particle physics. Atomic energy. Radioactivity ,QC770-798 ,Crystallography ,QD901-999 - Abstract
A machine-learning-based beam optimizer has been implemented to maximize the spectral brightness of the X-ray free-electron laser (XFEL) pulses of SACLA. A new high-resolution single-shot inline spectrometer capable of resolving features of the order of a few electronvolts was employed to measure and evaluate XFEL pulse spectra. Compared with a simple pulse-energy-based optimization, the spectral width was narrowed by half and the spectral brightness was improved by a factor of 1.7. The optimizer significantly contributes to efficient machine tuning and improvement of XFEL performance at SACLA.
- Published
- 2023
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