High-repetition-rate (HRR) experiments can collect large datasets with high temporal, spatial, and/or parametric resolution or large numbers of repeat measurements for statistics. HRR experiments also enable new experimental designs, including active feedback control loops and novel diagnostics, that can improve the reproducibility as well as the quantity of measurements. Together, these attributes make HRR experiments ideal for performing high-quality repeatable science. Until recently, these techniques have not been applied to high-energy-density–physics (HEDP) experiments, which are typically restricted to repetition rates of a few per day. However, recent advancements in lasers, pulsed-power drivers, target fabrication, and diagnostics are starting to change this fact, opening an exciting new frontier of HRR HEDP experiments. A mini-conference on this subject at the 2021 meeting of the American Physical Society Division of Plasma Physics brought together members of this growing community. The “High Repetition Rate Frontier in High-Energy-Density Physics” special topic in Physics of Plasmas highlights current progress in this exciting area.

Most current-generation large-scale, high-energy-density–physics (HEDP) facilities operate at low repetition rates (LRR, here defined as <ten shots/hour, but in practice often considerably lower), limited by the repetition rates of their drivers (lasers and/or pulsed-power), targets, and/or diagnostics. As a result, most experiments designed for these facilities have been limited to a small number of datapoints, often necessitating difficult choices between spatial resolution, temporal resolution, energy resolution, variation of experimental parameters, and experiment statistics. The HEDP community is severely data starved, and the demand for shots greatly exceeds the supply. However, recent advancements in laser and pulsed power drivers, target fabrication, and diagnostics are starting to enable higher repetition rates, opening an exciting new frontier of high-repetition-rate (HRR) HEDP experiments.

HRR experiments can collect data at rates ranging from ten shots/hour to thousands of shots per minute but are differentiated from LRR experiments by a few common features. In this regime, drivers, targets, and diagnostics must be automated to allow the experiment to be reset between shots with minimal human intervention. Data must be pre-processed automatically and analysis must employ data science and statistical techniques to be tractable. Both the challenges and the advantages of HRR experiments scale approximately with the repetition rate.

Experiments operating at HRR make it possible to collect much larger datasets than can feasibly be collected at LRR. This enables new modes of experimental operation.1 HRR experiments can scan experimental variables and diagnostic settings to collect datasets with high spatial, temporal, and parametric resolution over high-dimensional parameter space.2 These datasets provide a comprehensive, multidimensional view of phenomena that complements ongoing LRR experiments and is well suited to the validation of numerical simulations. These datasets are ideal for studying nonlinear and parametric processes (e.g., turbulence and laser–plasma interactions) or phenomena that span multiple regimes (e.g., magnetic reconnection). The size of these datasets also allows the ready application of big data techniques, such as machine learning and artificial intelligence (AI and ML).3,4 Large datasets also enable the use of statistical techniques. Low-signal events otherwise lost in noise can be observed in the mean of many identical shots, while large datasets enable the statistical interrogation of stochastic processes (e.g., turbulence spectra). The ability to take many repeat shots also allows quantification of shot-to-shot reproducibility (rarely verified in LRR experiments), examination of system-driven variability, as well as reduced uncertainty in measurements.

HRR HEDP experiments can also make use of closed-loop feedback control architectures in which controls, diagnostics, analysis, AI and ML codes, and simulations are all connected in a “self-steering” experiment.5 This methodology has already been employed to optimize the properties of particle beams accelerated by target normal sheath acceleration (TNSA) and laser-plasma wakefield accelerators. Closed-loop experiments could be configured to explore a large complex parameter space using a Markov Chain Monte Carlo algorithm or to optimize a quantity of interest (e.g., fusion yield). The large number of shots available at HRR is required to make these techniques feasible. Control loops could also automatically adjust laser pulse shapes or optical alignment to deliver more precise and repeatable performance or automate target positioning. Eventually, target manufacturing and characterization could also be included in these loops. Integrating multiple experimental systems into a control loop would allow measurements from multiple diagnostic sources to be incorporated to make adjustments (e.g., correcting systematic target and/or laser misalignment in inertial confinement fusion implosions based on inferred hot-spot velocities). These types of self-correcting control systems will make HRR experiments more robust against shot-to-shot variability, increasing the quality and repeatability of measurements.

A recent National Academy of Sciences report6 recognized the necessity of HRR to realize the potential of high-intensity/high-average-power short-pulse lasers. For example, laser-accelerated proton beams must be operated at HRR in order to provide the luminosity required for particle physics experiments or cancer therapy.7,8 Short-pulse experiments at HRR include measurements of spatial structures and parametric dependencies of laser wakefield acceleration, TNSA,9,10 nonlinear interactions of particle beams, and other high-intensity laser phenomena. Large datasets with highly repeatable shots will enable statistical sampling of rare events at the frontier of intense laser physics, such as measurements of ultra-relativistic laser-particle interactions, and strong quantum electrodynamics processes, such as Breit–Wheeler pair production and vacuum birefringence.

The development of HRR HEDP capabilities shares many technical requirements with the development of inertial fusion energy (IFE).11 Consequently, solving technical challenges to enable HRR HEDP experiments will also directly contribute to the development of IFE. Furthermore, conducting IFE-relevant experiments at HRR could accelerate scientific progress toward IFE. High-flux, HRR laser-driven neutron sources could be used for materials damage testing necessary for all fusion concepts.12 HRR experiments are well suited to studying many IFE-relevant HEDP topics, such as hydrodynamic instabilities and laser–plasma interactions (LPIs).13 Long-pulse HRR laser experiments, especially those coupled with short-pulse HRR backlighters, could provide high-temporal-resolution measurements of hydrodynamic instability growth. As highly nonlinear parametric processes, LPIs are a natural application for high-resolution parameter scans. In both cases, HRR experiments would also allow rapid testing of mitigation strategies to minimize the effect of these instabilities. Large datasets collected at HRR would improve the application of statistical models like those that have led to record yields at the Omega Laser Facility,14,15 which are currently severely limited by the number of data points. Relative to the extremely LRR of current IFE-relevant experimental platforms (one or a few shots per day), even a modest increase in repetition rates would be transformative.

While adapting HEDP experiments to HRR still presents technical challenges, significant progress has been made in this direction in the past two decades. Efforts are under way to develop HRR targets for both HEDP9,16,17 and IFE18,19 experiments. New detector technologies are being developed to replace those incompatible with HRR operation (e.g., film or scintillators)20–22 while AI and ML are being applied to automate data pre-processing at HRR.21,23 Many high-intensity short-pulse24 and several moderately high-energy (1kJE100J) long-pulse25–28 HRR laser systems have been built over the past two decades. The proposed upgrade to the Matter in Extreme Conditions experimental station of the Linear Coherent Light Source at the SLAC National Accelerator Laboratory would include a 1 kJ, long-pulse laser with a repetition rate of two shots/hour. The invention of the linear transformer driver is enabling HRR pulsed-power experiments,29,30 although HRR targets for these experiments remain a challenge. The commercial success of HRR liquid-tin-jet laser-plasma extreme ultraviolet light sources for lithography exemplifies the feasibility of a highly repeatable HRR integrated system.31 

Throughout HEDP, the high volume of shots (and corresponding low cost-per-shot) available at HRR lowers the cost to exploring innovative concepts. These attributes, as well as the cutting-edge experimental techniques required, also make HRR facilities ideal platforms for training students. While some technical challenges remain, the HRR experiments discussed in the mini-conference and featured in the High Repetition Rate Frontier in High-Energy-Density Physics special topic in Physics of Plasmas represent an exciting and potentially transformative new frontier in HEDP.

The mini-conference the High Repetition Rate Frontier in High-Energy-Density Physics was held at the 63rd American Physical Society Division of Plasma Physics meeting on November 9, 2021 in Pittsburgh, PA. The session included 11 oral presentations and two posters, covering a range of topics from HRR facilities, drivers, diagnostics, targets, and applications artificial intelligence and machine learning.

In the High Repetition Rate Frontier in High-Energy-Density Physics special topic in Physics of Plasmas, we highlight some of the exciting recent progress in this emerging scientific frontier.

  • “High repetition rate mapping of the interaction between a laser plasma and magnetized background plasma via laser induced fluorescence” (Dorst et al.) presents spatially resolved measurements of coupling between a laser-produced plasma and a magnetized background plasma using magnetic flux probes and a laser-induced fluorescence diagnostic at high repetition rate.

  • “Enhanced analysis of experimental x-ray spectra through deep learning” (Mariscal et al.) discusses the use of neural networks to automatically extract temperature, density, and charge-state distributions from x-ray spectra at sufficient speed to enable real-time analysis during high-repetition-rate experiments.

  • “High repetition rate diagnostics with integrated machine learning analysis for a new paradigm of actively controlled high intensity laser-solid experiments” (Scott et al.) discusses the integration of high-repetition rate-drivers, diagnostics, and machine learning for active feedback loop experiments with high-intensity laser–solid interactions.

  • “Ambient-temperature liquid jet targets for high-repetition-rate HED discovery science” (Treffert et al.) presents a tungsten microfluidic nozzle used to create an ambient-temperature planar liquid jet target for high-repetition-rate (1 kHz) laser-driven ion–acceleration experiments.

This material is based upon work supported by the Department of Energy National Nuclear Security Administration under Award Nos. DE-NA0003856 and DE-SC0020431, the University of Rochester, and the New York State Energy Research and Development Authority.

This report was prepared as an account of work sponsored by an agency of the U.S. Government. Neither the U.S. Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the U.S. Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the U.S. Government or any agency thereof.

The authors have no conflicts to disclose.

Peter Ver Bryck Heuer: Conceptualization (equal); Writing – original draft (lead); Writing – review & editing (equal). Scott Feister: Conceptualization (equal); Writing – review & editing (equal). Derek Brandon Schaeffer: Conceptualization (equal); Writing – review & editing (equal). Hans George Rinderknecht: Conceptualization (equal); Writing – review & editing (equal).

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

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