Adaptive control of laser-wakefield accelerators driven by mid-IR laser pulses

Lin, Jingquan; Ma, Y.; Schwartz, R.; Woodbury, Daniel; Nees, J.; Mathis, Mark; Thomas, Alexander; Krushelnick, K.; Milchberg, H. M.

doi:10.1364/oe.27.010912

Cited by 17 publications

(14 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several key experiments confirmed the fundamental feasibility of applying machine learning techniques for the real-time optimisation of plasma-based acceleration of electrons [296][297][298][299] and ions [300,301]. These experiments utilised genetic algorithms to control specific aspects of the experiment, such as the spatial or spectral phase of the driving laser and in some cases demonstrated optima with order-of-magnitude improvements over manual system optimisation or found significant improvements with unexpected driver properties.…”

Section: H Control and Optimisation Of Plasma Accelerator Experimentsmentioning

confidence: 82%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

View full text Add to dashboard Cite

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...

show abstract

Section: H Control and Optimisation Of Plasma Accelerator Experimentsmentioning

confidence: 82%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…To address this, we later present results from a 3D simulation that uses the optimal target from the 1D simulations. Despite these limitations, the prior success of evolutionary algorithms in related fields [20,23,[25][26][27][28][29] demonstrates the power of this approach, and in this work, we find it to be advantageous for optimizing ion acceleration.…”

Section: D Pic Optimization Driven By Evolutionary Algorithmsmentioning

confidence: 85%

“…Due to the complexity of ultra-intense laser interactions, rather than explore the large simulation parameter space essentially by hand or some other means, instead we use an evolutionary algorithm with a series of thousands of one-dimensional (1D) particle-in-cell (PIC) simulations to optimize the laser plasma interaction. The wider field of plasma physics is beginning to embrace statistical methods for various problems such as inertial confinement fusion [19][20][21][22], magnetic fusion [23,24], x-ray production [25], laser-wakefield acceleration [26,27], and to optimize the laser focus for electron or ion acceleration experiments [28,29]. To our knowledge, the present study is the first to directly optimize laser-based ion acceleration with such an approach.…”

Section: Introductionmentioning

confidence: 99%

Optimizing laser–plasma interactions for ion acceleration using particle-in-cell simulations and evolutionary algorithms

et al. 2020

View full text Add to dashboard Cite

The development of ultra-intense laser-based sources of high energy ions is an important goal, with a variety of potential applications. One of the barriers to achieving this goal is the need to maximize the conversion efficiency from laser energy to ion energy. We apply a new approach to this problem, in which we use an evolutionary algorithm to optimize conversion efficiency by exploring variations of the target density profile with thousands of one-dimensional particle-in-cell (PIC) simulations. We then compare this ‘optimal’ target identified by the one-dimensional PIC simulations to more conventional choices, such as with an exponential scale length pre-plasma, with fully three-dimensional PIC simulations. The optimal target outperforms the conventional targets in terms of maximum ion energy by 20% and show a noticeable enhancement of conversion efficiency to >2 MeV ions. This target geometry enhances laser coupling to the electrons, while still allowing the laser to strongly reflect from an effectively thin target. These results underscore the potential for this statistics-driven approach to guide research into optimizing laser–plasma simulations and experiments.

show abstract

“…With the fast development of high-repetition-rate operation capabilities in plasma targetry, high-power laser-plasma experiments can employ statistical methods that require a large number of shots. Studies for real-time optimization using evolutionary algorithms have been reported in recent years [6][7][8][9][10][11] . As the size of data to process has continued to increase, more advanced machine learning models have attracted increasing attention.…”

Section: Introductionmentioning

confidence: 99%

Applications of object detection networks in high-power laser systems and experiments

Lin

Haberstroh²,

Karsch³

et al. 2023

High Pow Laser Sci Eng

Self Cite

View full text Add to dashboard Cite

The recent advent of deep artificial neural networks has resulted in a dramatic increase in performance for object classification and detection. While pre-trained with everyday objects, we find that a state-of-the-art object detection architecture can very efficiently be fine-tuned to work on a variety of object detection tasks in a high-power laser laboratory. In this manuscript, three exemplary applications are presented. We show that the plasma waves in a laserplasma accelerator can be detected and located on the optical shadowgrams. The plasma wavelength and plasma density are estimated accordingly. Furthermore, we present the detection of all the peaks in an electron energy spectrum of the accelerated electron beam, and the beam charge of each peak is estimated accordingly. Lastly, we demonstrate the detection of optical damage in a high-power laser system. The reliability of the object detector is demonstrated over one thousand laser shots in each application. Our study shows that deep object detection networks are suitable to assist online and offline experiment analysis, even with small training sets. We believe that the presented methodology is adaptable yet robust, and we encourage further applications in Hz-level or KHz-level high-power laser facilities regarding the control and diagnostic tools, especially for those involving image data.

show abstract

Adaptive control of laser-wakefield accelerators driven by mid-IR laser pulses

Cited by 17 publications

References 42 publications

2022 Review of Data-Driven Plasma Science

2022 Review of Data-Driven Plasma Science

Optimizing laser–plasma interactions for ion acceleration using particle-in-cell simulations and evolutionary algorithms

Applications of object detection networks in high-power laser systems and experiments

Contact Info

Product

Resources

About