Fr. 207.00

Machine Learning Based Optimization of Laser-Plasma Accelerators

English · Hardback

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Description

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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.

List of 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.

Product details

Authors Sören Jalas
Publisher Springer, Berlin
 
Languages English
Product format Hardback
Released 24.05.2025
 
EAN 9783031880827
ISBN 978-3-0-3188082-7
No. of pages 134
Dimensions 155 mm x 11 mm x 235 mm
Weight 405 g
Illustrations XXXVII, 134 p. 64 illus., 63 illus. in color.
Series Springer Theses
Subjects Natural sciences, medicine, IT, technology > Physics, astronomy > Miscellaneous

Optimierung, machine learning, Maschinelles Lernen, Optimization, Teilchen- und Hochenergiephysik, Accelerator Physics, Plasma-based Accelerators, Electron beams, Synchrotron light sources, Laser-plasma interaction, ANGUS laser system, LUX Laser-Plasma Accelerator, Bayesian optimization, Beam quality optimization, LPA systems, Beam tuning

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