Full Description
This book explores the application of machine learning-based methods, particularly Bayesian optimization, within the realm of laser-plasma accelerators. The book involves the implementation of Bayesian optimization to fine tune the parameters of the lux accelerator, encompassing simulations and real-time experimentation.
In combination, the methods presented in this book provide valuable tools for effectively managing the inherent complexity of LPAs, spanning from the design phase in simulations to real-time operation, potentially paving the way for LPAs to cater to a wide array of applications with diverse demands.
Contents
Principles of Laser-Plasma Acceleration.- Bayesian Optimization.- Bayesian Optimization of Plasma Accelerator Simulations.- Experimental Setup: The LUX Laser-Plasma Accelerator.- Bayesian Optimization of a Laser-Plasma Accelerator.- Tuning Curves for a Laser-Plasma Accelerator.- Conclusion.
