Automation and control of laser wakefield accelerators using Bayesian optimization

Abstract

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%.

One-shot identification of SARS-CoV-2 S RBD escape mutants using yeast screening

“…This population was not labeled with biotinylated ACE2 and so serves as a null experiment where the observed enrichment ratios are due to other sources of variance and not to differential nAb binding. We fit the distribution of enrichment ratios for each of these control samples using kernel density estimation (KDE) (SciPy's scipy.stats.gaussian_kde with default parameters) (Shalloo et al, 2020). We then treated this distribution estimate as an empirical null hypothesis.…”

Adaptive Machine Learning for Robust Diagnostics and Control of Time-Varying Particle Accelerator Components and Beams

“…In 1985, a solution was proposed for the simple time-invariant case where b(t) ≡ b was an unknown constant value [37,38], this solution suffered from an unbounded growing overshoot for time-varying b(t) with changing sign, eventually destabilizing and destroying any physical system. In 2012, problem (16) was finally solved with a novel model-independent approach [39,40], whose feedback forces the dynamics (16) to have an average behavior described by a new system of the form˙x…”

“…with arbitrarily small > 0, where the unknown control direction, b(t), has become squared and can be stabilized automatically. Because of arbitrarily close proximity of the trajectories of ( 17) to (16), stabilization of ( 17) is equivalent to stabilizing (16).…”

“…Recent ML applications for accelerators include ML-enhanced genetic optimization [5], utilizing surrogate models for simulation-based optimization studies and for estimating beam characteristics [6][7][8][9][10], Bayesian and GP approaches for accelerator tuning [11][12][13][14][15][16], various applications at the Large Hardon Collider including optics corrections and detecting faulty beam position monitors [17][18][19], powerful polynomial chaos expansion-based surrogate models for uncertainty quantification have been developed [20], and RL tools have been developed for online accelerator optimization [21][22][23][24][25]. One challenge faced by many ML approaches is the fact that as accelerators and their beams change with time, the ML models that were trained with previously collected data are no longer accurate because they are being applied to a different system than the one which they have been trained for.…”

Towards ML-Based Diagnostics of Laser–Plasma Interactions

“…Machine learning (ML) methods open novel ways for solving long-standing problems in many areas of physics, including plasma physics [1][2][3][4], condensed-matter physics [5,6], quantum physics [7][8][9][10], thermodynamics [11], quantum chemistry [12], particle physics [13] and many others [14,15]. One prominent problem for ML methods is to generalize results of numerical simulations, such that they can be used to extract unknown information from experimentally measured data.…”