Machine Learning Based Optimization of Laser-Plasma Accelerators
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Beschreibung
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.
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.
Nominated as an outstanding thesis by Hamburg University and Desy Features machine learning-based methods to optimize laser-plasma accelerators Identifies the methods for autonomous tuning on generating high quality electron beams
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Themen in »Machine Learning Based Optimization of Laser-Plasma Accelerators«
LPA systems Laser-plasma interaction Beam tuning Bayesian optimization Electron beams LUX Laser-Plasma Accelerator ANGUS laser system Beam quality optimization Synchrotron light sources