Laser-Accelerated, Low-Divergence 15-MeV Quasimonoenergetic Electron Bunches at 1 kHz

Salehi, Fatholah; Le, M.; Railing, L.; Kolesík, M.; Milchberg, H. M.

doi:10.1103/physrevx.11.021055

Cited by 37 publications

(27 citation statements)

References 71 publications

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“…Today, building on the results of these early experiments, the field is making significant progress in im-proving the reliability of the acceleration process to allow for stable long term operation [307,308]. Additionally, promising progress has been made in using lowenergy high-repetition-rate drivers [309,310] and highpower high-repetition-rate laser drivers are foreseeable in the near future.…”

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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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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“…Laser-wakefield-accelerators (LWFA) have the technological potential to supplant conventional-radiofrequency-accelerators and also bring about a new generation of compact-tabletop-accelerators 1,2 . At present, LWFAs can produce stable electron beams with ultrashort duration, GeV-scale energy, and very low emittance from centimeter-scale acceleration stages at high repetition rates [3][4][5] . These high energy and brightness electron beams are essential to meeting the demands of future accelerators such as next-generation colliders 6 or compact free-electron lasers 7 .…”

Section: Introductionmentioning

confidence: 99%

Impact of Electron Transport Models on Capillary Discharge Plasmas

Diaw,

Coleman,

Cook

et al. 2022

Preprint

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Magnetohydrodynamics (MHD) can be used to model capillary discharge waveguides in laser-wakefield accelerators. However, the predictive capability of MHD can suffer due to poor microscopic closure models. Here, we study the impact of electron heating and thermal conduction on capillary waveguide performance as part of an effort to understand and quantify uncertainties in modeling and designing next-generation plasma accelerators. To do so, we perform two-dimensional high-resolution MHD simulations using an argon-filled capillary discharge waveguide with three different electron transport coefficients models. The models tested include (i) Davies et al. (ii) Spitzer, and (iii) Epperlein-Haines (EH). We found that the EH model overestimates the electron temperature inside the channel by over 20% while predicting a lower azimuthal magnetic field. Moreover, the Spitzer model, often used in MHD simulations for plasma-based accelerators, predicts a significantly higher electron temperature than the other models suggest.

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“…In most cases, the laser pulses used to drive the LWFA are sufficiently long so that the interaction with the plasma can be described through the widely used cycle-averaged ponderomotive framework [7], where the physics depends only on the envelope of the pulse, and is polarization independent. But in high-repetition rate laser plasma accelerators capable of producing kilohertz electron beams at MeV energies [8][9][10], shorter, sub-5 fs pulses are used. When these near-single cycle pulses drive a wakefield, the ponderomotive approximation breaks down and asymmetries arise in the electron dynamics.…”

Section: Introductionmentioning

confidence: 99%

Carrier-envelope phase controlled dynamics of relativistic electron beams in a laser-wakefield accelerator

Rovige,

Monzac,

Huijts

et al. 2022

Preprint

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In laser-wakefield acceleration, an ultra-intense laser pulse is focused into an underdense plasma in order to accelerate electrons to relativistic velocities. In most cases, the pulses consist of multiple optical cycles and the interaction is well described in the framework of the ponderomotive force where only the envelope of the laser has to be considered. But when using single-cycle pulses, the ponderomotive approximation breaks down, and the actual waveform of the laser has to be taken into account. In this paper, we use near-single cycle laser pulses to drive a laser-wakefield accelerator. We observe variations of the electron beam pointing on the order of 10 mrad in the polarisation direction, as well as 30% variations of the beam charge, locked to the value of the controlled laser carrierenvelope phase, in both nitrogen and helium plasma. Those findings are explained through particle-incell simulations indicating that low-emittance, ultra-short electron bunches are periodically injected off-axis by the transversally oscillating bubble associated with the slipping carrier-envelope phase.

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Laser-Accelerated, Low-Divergence 15-MeV Quasimonoenergetic Electron Bunches at 1 kHz

Cited by 37 publications

References 71 publications

2022 Review of Data-Driven Plasma Science

2022 Review of Data-Driven Plasma Science

Impact of Electron Transport Models on Capillary Discharge Plasmas

Carrier-envelope phase controlled dynamics of relativistic electron beams in a laser-wakefield accelerator

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