Controlling laser driven protons acceleration using a deformable mirror at a high repetition rate

Noaman-ul-Haq, Muhammad; Sokollik, Thomas; Ahmed, H.; Braenzel, J.; Ehrentraut, L.; Mirzaie, Maryam; Yu, Linpeng; Sheng, Zheng-Ming; Chen, L.M.; Schnürer, M.; Zhang, J.

doi:10.1016/j.nima.2017.11.075

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…The electron beam charge, energy spectra and beam pointing have been optimized. The combination of genetic algorithm and DM has been applied to other high-power laser experiments as well [215][216][217][218][219] .…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Data-driven science and machine learning methods in laser–plasma physics

Döpp

Eberle

Howard

et al. 2023

High Pow Laser Sci Eng

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Laser-plasma physics has developed rapidly over the past few decades as lasers have become both more powerful and more widely available. Early experimental and numerical research in this field was dominated by single-shot experiments with limited parameter exploration. However, recent technological improvements make it possible to gather data for hundreds or thousands of different settings in both experiments and simulations. This has sparked interest in using advanced techniques from mathematics, statistics and computer science to deal with, and benefit from, big data. At the same time, sophisticated modeling techniques also provide new ways for researchers to deal effectively with situation where still only sparse data are available. This paper aims to present an overview of relevant machine learning methods with focus on applicability to laser-plasma physics and its important sub-fields of laser-plasma acceleration and inertial confinement fusion.

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Section: Genetic Algorithmsmentioning

confidence: 99%

Data-driven science and machine learning methods in laser–plasma physics

Döpp

Eberle

Howard

et al. 2023

High Pow Laser Sci Eng

View full text Add to dashboard Cite

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

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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.

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Beyond optimization—supervised learning applications in relativistic laser-plasma experiments

et al. 2021

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We explore the applications of a variety of machine learning techniques in relativistic laser-plasma experiments beyond optimization purposes. With the trained supervised learning models, the beam charge of electrons produced in a laser wakefield accelerator is predicted given the laser wavefront change caused by a deformable mirror. Feature importance analysis using the trained models shows that specific aberrations in the laser wavefront are favored in generating higher beam charges, which reveals more information than the genetic algorithms and the statistical correlation do. The predictive models enable operations beyond merely searching for an optimal beam charge. The quality of the measured data is characterized, and anomaly detection is demonstrated. The model robustness against measurement errors is examined by applying a range of virtual measurement error bars to the experimental data. This work demonstrates a route to machine learning applications in a highly nonlinear problem of relativistic laser-plasma interaction for in-depth data analysis to assist physics interpretation.

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Controlling laser driven protons acceleration using a deformable mirror at a high repetition rate

Abstract: Controlling laser driven proton acceleration using a deformable mirror at a high repetition rate, Nuclear Inst. and Methods in

Cited by 5 publications

References 27 publications

Data-driven science and machine learning methods in laser–plasma physics

Data-driven science and machine learning methods in laser–plasma physics

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

Beyond optimization—supervised learning applications in relativistic laser-plasma experiments

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