Laser wakefield accelerators promise to revolutionize many areas of accelerator science. However, one of the greatest challenges to their widespread adoption is the difficulty in control and optimization of the accelerator outputs due to coupling between input parameters and the dynamic evolution of the accelerating structure. Here, we use machine learning techniques to automate a 100 MeV-scale accelerator, which optimized its outputs by simultaneously varying up to six parameters including the spectral and spatial phase of the laser and the plasma density and length. Most notably, the model built by the algorithm enabled optimization of the laser evolution that might otherwise have been missed in single-variable scans. Subtle tuning of the laser pulse shape caused an 80% increase in electron beam charge, despite the pulse length changing by just 1%.

/pdf/automation-and-control-of-laser-wakefield-accelerators-using-58oljlb05s.pdf

Automation and control of laser wakefield accelerators using Bayesian optimization.

Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. This Review gives a snapshot of nuclear physics research which has been transformed by machine learning techniques. 

/pdf/i-colloquium-i-machine-learning-in-nuclear-physics-1l7ycfp1.pdf

Colloquium : Machine learning in nuclear physics

High-fidelity physics simulations are powerful tools in the design and optimization of charged particle accelerators. However, the computational burden of these simulations often limits their use in practice for design optimization and experiment planning. It also precludes their use as online models tied directly to accelerator operation. We introduce an approach based on machine learning to create nonlinear, fast-executing surrogate models that are informed by a sparse sampling of the physics simulation. The models are 10^6 to 10^7 times more efficient to execute.We also demonstrate that these models can be reliably used with multi-objective optimization to obtain orders-of-magnitude speedup in initial design studies and experiment planning. For example, we required 132 times fewer simulation evaluations to obtain an equivalent solution for our main test case, and initial studies suggest that between 330 to 550 times fewer simulation evaluations are needed when using an iterative retraining process. Our approach enables new ways for high-fidelity particle accelerator simulations to be used, at comparatively little computational cost.

pdf/machine-learning-for-orders-of-magnitude-speedup-in-multi-2lz7gf53k3.pdf

Machine Learning for Orders of Magnitude Speedup in Multi-Objective Optimization of Particle Accelerator Systems.

Toward the end-to-end optimization of particle physics instruments with differentiable programming

The ability to prepare nonclassical states in a robust manner is essential for quantum sensors beyond the standard quantum limit. We demonstrate that Bayesian optimal control is capable of finding control pulses that drive trapped Rydberg atoms into highly entangled Greenberger--Horne--Zeilinger states. The control sequences have a physically intuitive functionality based on the quasi-integrability of the Ising dynamics. They can be constructed in laboratory experiments, resulting in preparation times that scale very favorably with the system size.

Bayesian Optimal Control of Greenberger-Horne-Zeilinger States in Rydberg Lattices.

The Linac coherent light source x-ray free-electron laser is a complex scientific apparatus which changes configurations multiple times per day, necessitating fast tuning strategies to reduce setup time for successive experiments. To this end, we employ a Bayesian approach to maximizing x-ray laser pulse energy by controlling groups of quadrupole magnets. A Gaussian process model provides probabilistic predictions for the machine response with respect to control parameters, enabling a balance of exploration and exploitation in the search for the global optimum. We show that the model parameters can be learned from archived scans, and correlations between devices can be extracted from the beam transport. The result is a sample-efficient optimization routine, combining both historical data and knowledge of accelerator physics to significantly outperform existing optimizers.

Bayesian Optimization of a Free-Electron Laser

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Bayesian Optimization of a Free-Electron Laser