Modeling laser-driven ion acceleration with deep learning

Djordjevic, Blagoje; Kemp, Andreas; Kim, J.; Simpson, Raspberry; Wilks, S. C.; Ma, T.; Mariscal, D.

doi:10.1063/5.0045449

Cited by 26 publications

(22 citation statements)

References 52 publications

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“…This approach has already been demonstrated to improve electron and X-ray beams from wakefield accelerators [65,66] and laser-driven proton acceleration in simulations [67] . Other machine learning techniques that have been applied in the study of laserplasma accelerators include neural networks [68,69] and evolutionary algorithms [70][71][72][73] .…”

Section: Optimizationmentioning

confidence: 99%

Optimization and control of synchrotron emission in ultraintense laser–solid interactions using machine learning

Goodman

King

Dolier

et al. 2023

High Pow Laser Sci Eng

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The optimum parameters for the generation of synchrotron radiation in ultraintense laser pulse interactions with planar foils are investigated with the application of Bayesian optimisation, via Gaussian process regression, to 2D particle-incell simulations. Individual properties of the synchrotron emission, such as the yield, are maximised, and simultaneous mitigation of bremsstrahlung emission is achieved with multi-variate objective functions. The angle-of-incidence of the laser pulse onto the target is shown to strongly influence the synchrotron yield and angular profile, with oblique incidence producing the optimal results. This is further explored in 3D simulations, in which additional control of the spatial profile of synchrotron emission is demonstrated by varying the polarisation of the laser light. The results demonstrate the utility of applying a machine learning-based optimisation approach and provide new insights into the physics of radiation generation in multi-petawatt laser-foil interactions, which will inform the design of experiments in the QED-plasma regime.

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Section: Optimizationmentioning

confidence: 99%

Optimization and control of synchrotron emission in ultraintense laser–solid interactions using machine learning

Goodman

King

Dolier

et al. 2023

High Pow Laser Sci Eng

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“…With the proliferation of multi-Hz high power laser pulses [34], and the development of HRR-compatible solid-density targetry [35][36][37][38][39][40][41], it is now possible to quickly obtain large datasets from laser-driven ion acceleration experiments. This opens the possibility to perform extensive multi-dimensional parameter scans to elucidate the interdependence of different experimental control parameters, as well as to apply machine learning techniques to optimise ion beam properties -within complex multi-dimensional parameter spaces -in automated experiments [42][43][44] and simulations [45,46].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Automated control and optimization of laser-driven ion acceleration

Loughran

Streeter

Ahmed

et al. 2023

High Pow Laser Sci Eng

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The interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space. This limits the utility of sequential 1D scanning of experimental parameters for the optimisation of secondary radiation, although to-date this has been the accepted methodology due to low data acquisition rates. High repetition-rate (HRR) lasers augmented by machine learning present a valuable opportunity for efficient source optimisation. Here, an automated, HRR-compatible system produced high fidelity parameter scans, revealing the influence of laser intensity on target pre-heating and proton generation. A closed-loop Bayesian optimisation of maximum proton energy, through control of the laser wavefront and target position, produced proton beams with equivalent maximum energy to manually-optimized laser pulses but using only 60% of the laser energy. This demonstration of automated optimisation of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources.

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“…In the context of plasma-based ion acceleration it has further been shown that surrogate models can replace costly simulations, based on training neural networks with comparably sparse sets of particle-in-cell simulations [305,306].…”

Section: H Control and Optimisation Of Plasma Accelerator Experimentsmentioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

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Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasmaprocessing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. CONTENTSI. Introduction 6 II. Fundamental Data Science 6 A. Introduction 6 B. Data Reduction/Compression 7 C. Dimensional reduction and sparse modeling 8 D. ML-enhanced modeling and simulation 9 E. ML Hardware and integration with models 10 F. Workflow Automation 11 G. Uncertainty Quantification 12 H. Visualization and Data Understanding 13 I. ML Control Theory 15 III. Basic Plasma Physics and Laboratory Experiments A. Introduction B. Spectroscopy, imaging and tomography C. Sparse measurement and noise D. Synthetic instruments and data E. Experimental data visualization F. High-rep rate laser experiments G. Charged particle beams H. Control and Optimisation of Plasma Accelerator Experiments I. Dusty and complex plasmas J. Physics and machine learning K. Challenges and outlook 5 IV. Magnetic Confinement Fusion 36 A. Introduction 36 B. Data-Driven Physics Models 36 C. Optimizing experimental workflows with data-driven methods 37 D. Diagnostics and Fusion Data Streams 38 E. Prediction of Tokamak Disruption 39 F. Surrogate models of fusion plasma 40 G. Magnetic Fusion Energy Data Challenges and Solutions 41 H. Data Science for extreme scale simulation 43 I. Challenges and outlook 44 V. Inertial confinement fusion and high-energy-density physics 45 A. Introduction 45 B. Representation learning for multimodal data 45 C. Transfer learning for simulation and experimen 47 D. Uncertainty quantification and Bayesian inference 47 E. High-performance computing and simulation acceleration 49 F. Design exploration and optimization 49 G. Self-driving experimental facilities 51 H. Challenges and outlook 52 VI. Space and astronomical plasmas 52 A. Introduction 52 B. Space and ground instruments 53 C. Space weather prediction 55 D. Transfer learning to improve historic data 56 E. Surrogate models of fluid closures using machine learning 56 F. Magnetic reconnection 58 G. Challenges and outlook 60 VII. Plasma tec...

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Modeling laser-driven ion acceleration with deep learning

Cited by 26 publications

References 52 publications

Optimization and control of synchrotron emission in ultraintense laser–solid interactions using machine learning

Optimization and control of synchrotron emission in ultraintense laser–solid interactions using machine learning

Automated control and optimization of laser-driven ion acceleration

2022 Review of Data-Driven Plasma Science

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