Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning

Sánchez‐González, Álvaro; Micaelli, Paul; Olivier, Carel P.; Barillot, T.; Ilchen, Markus; Lutman, Alberto; Marinelli, Agostino; Maxwell, Timothy; Achner, A.; Agåker, Marcus; Berrah, N.; Bostedt, Christoph; Bozek, John D.; Buck, Jens; Bucksbaum, P. H.; Montero, S. Carron; Cooper, Bridgette; Cryan, James; Dong, Minjie; Feifel, Raimund; Frasinski, L. J.; Fukuzawa, H.; Galler, Andreas; Hartmann, Gregor; Hartmann, Nils; Helml, Wolfram; Johnson, Allan S.; Knie, André; Lindahl, Anton; Liu, J.; Motomura, K.; Mucke, Melanie; O'Grady, Caroline; Rubensson, Jan‐Erik; Simpson, E. R.; Squibb, Richard J.; Såthe, Conny; Ueda, Kiyoshi; Vacher, Morgane; Walke, D. J.; Zhaunerchyk, Vitali; Coffee, Ryan; Marangos, Jonathan P.

doi:10.1038/ncomms15461

Cited by 86 publications

(63 citation statements)

References 52 publications

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“…[5]. Finally, there is growing interest due to recent studies which highlight the versatility of ML methods used in particle accelerators as tools for prediction, control and optimization of accelerator performance [6][7][8][9][10]. As an example, recent work performed at Fermilab's FAST facility has been aimed at training a virtual diagnostic to predict the e-beam emittance through a combination of simulation and experimental studies [5,11].…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based longitudinal phase space prediction of particle accelerators

Emma

Edelen

Hogan

et al. 2018

Phys. Rev. Accel. Beams

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We report on the application of machine learning (ML) methods for predicting the longitudinal phase space (LPS) distribution of particle accelerators. Our approach consists of training a ML-based virtual diagnostic to predict the LPS using only nondestructive linac and e-beam measurements as inputs. We validate this approach with a simulation study for the FACET-II linac and with an experimental demonstration conducted at LCLS. At LCLS, the e-beam LPS images are obtained with a transverse deflecting cavity and used as training data for our ML model. In both the FACET-II and LCLS cases we find good agreement between the predicted and simulated/measured LPS profiles, an important step towards showing the feasibility of implementing such a virtual diagnostic on particle accelerators in the future.

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Section: Introductionmentioning

confidence: 99%

Machine learning-based longitudinal phase space prediction of particle accelerators

Emma

Edelen

Hogan

et al. 2018

Phys. Rev. Accel. Beams

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“…X‐ray diffraction (XRD) data can also be analyzed by ML . In the face of large‐scale measurement data with high‐throughput characterization, it will undoubtedly consume a lot of time and energy if we analyze them one by one and find sample data of interest from them.…”

Section: Ai Applications For Materials Science and Engineeringmentioning

confidence: 99%

Artificial Intelligence to Power the Future of Materials Science and Engineering

Sha

Guo

Qing

et al. 2020

Advanced Intelligent Systems

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Artificial intelligence (AI) has received widespread attention over the last few decades due to its potential to increase automation and accelerate productivity. In recent years, a large number of training data, improved computing power, and advanced deep learning algorithms are conducive to the wide application of AI, including material research. The traditional trial‐and‐error method is inefficient and time‐consuming to study materials. Therefore, AI, especially machine learning, can accelerate the process by learning rules from datasets and building models to predict. This is completely different from computational chemistry where a computer is only a calculator, using hard‐coded formulas provided by human experts. Herein, the application of AI in material innovation is reviewed, including material design, performance prediction, and synthesis. The realization details of AI techniques and advantages over conventional methods are emphasized in these applications. Finally, the future development direction of AI is expounded from both algorithm and infrastructure aspects.

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“…Recent trends in machine learning have had a notable, positive impact on the needs for expert operators [117,118]. The UC-XFEL may serve as a platform form exploring advanced methods in machine learning for free-electron laser [119] and associated acceleration and beam transport systems [120]. Conversely, the refinement of such methods should permit much smaller teams of expert personnel to operate the UC-XFEL.…”

Section: Physics Applicationsmentioning

confidence: 99%

Towards an ultra-compact x-ray free-electron laser (Conference Presentation)

Rosenzweig

2019

Advances in Laboratory-Based X-Ray Sources, Optics, and Applications VII

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In the field of beam physics, two frontier topics have taken center stage due to their potential to enable new approaches to discovery in a wide swath of science. These areas are: advanced, high gradient acceleration techniques, and xray free electron lasers (XFELs). Further, there is intense interest in the marriage of these two fields, with the goal of producing a very compact XFEL. In this context, recent advances in high gradient radio-frequency cryogenic copper structure research have opened the door to the use of surface electric fields between 250 and 500 MV/m. Such an approach is foreseen to enable a new generation of photoinjectors with sixdimensional beam brightness beyond the current state-of-the-art by well over an order of magnitude. This advance is an essential ingredient enabling an ultra-compact XFEL (UC-XFEL). In addition, one may accelerate these bright beams to GeV scale in less arXiv:2003.06083v1 [physics.acc-ph]An Ultra-Compact X-Ray Free-Electron Laser 2 than 10 meters. Such an injector, when combined with inverse free electron laserbased bunching techniques can produce multi-kA beams with unprecedented beam quality, quantified by 50 nm-rad normalized emittances. These beams, when injected into innovative, short-period (1-10 mm) undulators uniquely enable UC-XFELs having footprints consistent with university-scale laboratories. We describe the architecture and predicted performance of this novel light source, which promises photon production per pulse of a few percent of existing XFEL sources. We review implementation issues including collective beam effects, compact x-ray optics systems, and other relevant technical challenges. To illustrate the potential of such a light source to fundamentally change the current paradigm of XFELs with their limited access, we examine possible applications in biology, chemistry, materials, atomic physics, industry, and medicine which may profit from this new model of performing XFEL science.

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Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning

Cited by 86 publications

References 52 publications

Machine learning-based longitudinal phase space prediction of particle accelerators

Machine learning-based longitudinal phase space prediction of particle accelerators

Artificial Intelligence to Power the Future of Materials Science and Engineering

Towards an ultra-compact x-ray free-electron laser (Conference Presentation)

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