Historical research guide · 1096 · 2021
Modeling laser-driven ion acceleration with deep learning
B. Z. Djordjević · A. J. Kemp · J. Kim · R. A. Simpson · S. C. Wilks · T. Ma · D. A. Mariscal
In one minute
A Lawrence Livermore group asked a practical question about the fastest ion beams made in a laboratory: when a short, intense laser pulse strikes a thin foil and blows a beam of ions off the back, which knobs actually set the ion energy? The honest way to answer is a particle-in-cell simulation, which follows the plasma particle by particle and costs so much computer time that sweeping the whole parameter space is out of reach. So Djordjević and colleagues ran a little over a thousand one-dimensional simulations and trained a neural network on them, producing a surrogate — a fast stand-in that maps laser intensity, pulse length, foil thickness, target density and the pre-plasma gradient scale length onto peak ion energy and hot-electron temperature. The surrogate answers in a heartbeat, so the whole space can be walked. What it turned up is the pre-plasma: ion energy peaks sharply when that thin front layer has a scale length near two tenths of a micron, a knob nobody was tuning.
Connection
Chapter 9 is about what plasma does when energy is poured into it faster than it can spread out, and laser-driven ion acceleration is where a laboratory reaches that regime today; chapter 12 keeps the ledger of the fusion routes those beams serve. The method carries as much weight as the physics — turning an expensive simulation into a surrogate you can turn like a dial is how a design programme finds its own optimum, and chapter 1 is where the site keeps the rules for trusting a result like that.
From the source
Peak ion energy and hot-electron temperature both track laser intensity most strongly, and then, in falling order, pulse duration, pre-plasma gradient scale length, target density and foil thickness — with the gradient scale length carrying more weight on ion energy than pulse duration does.Section IV C; correlation matrix, Figure 7(a)
Ion energy peaks as a function of the pre-plasma gradient scale length near two tenths of a micron, a swing of more than ten mega-electronvolts, which points to a distinctive coupling between the laser and the plasma its own leading edge has already made.Sections IV C and IV D; Figures 7(g) and 9
At fixed laser energy — intensity multiplied by pulse duration held constant — shorter and more intense pulses accelerate ions better than longer, weaker ones throughout the sub-picosecond range sampled here, and the energy gain flattens off once the pulse runs longer than about 150 femtoseconds.Section IV C; constant-energy curves, Figure 8(b) and 8(c)
An ensemble of more than a thousand one-dimensional EPOCH particle-in-cell runs is enough to train a multilayer network into a working surrogate: the best models reached a mean-squared error of five parts in a million or less on outputs normalised to the range zero to one, in one to two hours of training on a single V100 graphics processor.Sections II A, III B and III C; Section IV A
A surrogate can invent structure that is not physics: a streak the network drew in the intensity–duration map between 120 and 180 femtoseconds survived several retrainings and standard regularisation, then vanished once a denser block of 100 simulations was added — so features found this way have to be validated before they are believed.Section IV C; Figure 8(a) compared with Figure 8(b)
The gradient-length feature is the result the authors want tested next, in higher-dimensional simulations, with shaped pulses, and out at higher intensities and thinner targets where other acceleration mechanisms — radiation-induced transparency, breakout afterburner, radiation-pressure acceleration — take over from sheath acceleration.Section V, conclusions
The way in
https://doi.org/10.1063/5.0045449How to cite it
B. Z. Djordjević, A. J. Kemp, J. Kim, R. A. Simpson, S. C. Wilks, T. Ma, D. A. Mariscal (2021) Modeling laser-driven ion acceleration with deep learning doi:10.1063/5.0045449