Spectral-brightness optimization of an X-ray free-electron laser by machine-learning-based tuning

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 an...

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Autores principales: Iwai, Eito, Inoue, Ichiro, Maesaka, Hirokazu, Inagaki, Takahiro, Yabashi, Makina, Hara, Toru, Tanaka, Hitoshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2023
Materias:
Research Papers
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10624030/
https://www.ncbi.nlm.nih.gov/pubmed/37885153
http://dx.doi.org/10.1107/S1600577523007737
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author Iwai, Eito
Inoue, Ichiro
Maesaka, Hirokazu
Inagaki, Takahiro
Yabashi, Makina
Hara, Toru
Tanaka, Hitoshi
author_facet Iwai, Eito
Inoue, Ichiro
Maesaka, Hirokazu
Inagaki, Takahiro
Yabashi, Makina
Hara, Toru
Tanaka, Hitoshi
author_sort Iwai, Eito
collection PubMed
description 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.
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spelling pubmed-106240302023-11-04 Spectral-brightness optimization of an X-ray free-electron laser by machine-learning-based tuning Iwai, Eito Inoue, Ichiro Maesaka, Hirokazu Inagaki, Takahiro Yabashi, Makina Hara, Toru Tanaka, Hitoshi J Synchrotron Radiat Research Papers 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. International Union of Crystallography 2023-10-27 /pmc/articles/PMC10624030/ /pubmed/37885153 http://dx.doi.org/10.1107/S1600577523007737 Text en © Eito Iwai et al. 2023 https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.
spellingShingle Research Papers
Iwai, Eito
Inoue, Ichiro
Maesaka, Hirokazu
Inagaki, Takahiro
Yabashi, Makina
Hara, Toru
Tanaka, Hitoshi
Spectral-brightness optimization of an X-ray free-electron laser by machine-learning-based tuning
title Spectral-brightness optimization of an X-ray free-electron laser by machine-learning-based tuning
title_full Spectral-brightness optimization of an X-ray free-electron laser by machine-learning-based tuning
title_fullStr Spectral-brightness optimization of an X-ray free-electron laser by machine-learning-based tuning
title_full_unstemmed Spectral-brightness optimization of an X-ray free-electron laser by machine-learning-based tuning
title_short Spectral-brightness optimization of an X-ray free-electron laser by machine-learning-based tuning
title_sort spectral-brightness optimization of an x-ray free-electron laser by machine-learning-based tuning
topic Research Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10624030/
https://www.ncbi.nlm.nih.gov/pubmed/37885153
http://dx.doi.org/10.1107/S1600577523007737
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