Following the 3.15 MJ fusion milestone at the National Ignition Facility, the further development of inertial confinement fusion, both as a source for future electricity generation and for high-energy-density physics applications, requires the development of more robust ignition concepts at current laser facility energy scales. This can potentially be achieved by auxiliary heating the hotspot of low convergence wetted foam implosions where hydrodynamic and parametric instabilities are minimized. This paper presents the first multi-dimensional Vlasov–Maxwell and particle-in-cell simulations to model this collisionless interaction, only recently made possible by access to the largest modern supercomputers. The key parameter of interest is the maximum fraction of energy that can be extracted from the electron beams into the hotspot plasma. The simulations indicate that significant coupling efficiencies are achieved over a wide range of beam parameters and spatial configurations. The implications for experimental tests on the National Ignition Facility are discussed.

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Knowledge of spatio-temporal couplings such as pulse-front tilt or curvature is important to determine the focused intensity of high-power lasers. Common techniques to diagnose these couplings are either qualitative or require hundreds of measurements. Here we present both a new algorithm for retrieving spatio-temporal couplings, as well as novel experimental implementations. Our method is based on the expression of the spatio-spectral phase in terms of a Zernike-Taylor basis, allowing us to directly quantify the coefficients for common spatio-temporal couplings. We take advantage of this method to perform quantitative measurements using a simple experimental setup, consisting of different bandpass filters in front of a Shack-Hartmann wavefront sensor. This fast acquisition of laser couplings using narrowband filters, abbreviated FALCON, is easy and cheap to implement in existing facilities. To this end, we present a measurement of spatio-temporal couplings at the ATLAS-3000 petawatt laser using our technique.

Presented is a novel way to combine snapshot compressive imaging and lateral shearing interferometry in order to capture the spatio-spectral phase of an ultrashort laser pulse in a single shot. A deep unrolling algorithm is utilised for the snapshot compressive imaging reconstruction due to its parameter efficiency and superior speed relative to other methods, potentially allowing for online reconstruction. The algorithm's regularisation term is represented using neural network with 3D convolutional layers, to exploit the spatio-spectral correlations that exist in laser wavefronts. Compressed sensing is not typically applied to modulated signals, but we demonstrate its success here. Furthermore, we train a neural network to predict the wavefronts from a lateral shearing interferogram in terms of Zernike polynomials, which again increases the speed of our technique without sacrificing fidelity. This method is supported with simulation-based results. While applied to the example of lateral shearing interferometry, the methods presented here are generally applicable to a wide range of signals, including Shack-Hartmann-type sensors. The results may be of interest beyond the context of laser wavefront characterization, including within quantitative phase imaging.