Machine Learning Based Optimization of Laser-Plasma Accelerators

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.

Inhaltsverzeichnis

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.

Produktdetails

• Autoren: Sören Jalas
• Verlag: Springer, Berlin
• Sprache: Englisch
• Produktform: Fester Einband
• Erschienen: 24.05.2025
• EAN: 9783031880827
• ISBN: 978-3-0-3188082-7
• Seiten: 134
• Abmessung: 155 mm x 11 mm x 235 mm
• Gewicht: 405 g
• Illustration: XXXVII, 134 p. 64 illus., 63 illus. in color.
• Serie: Springer Theses
• Themen: Naturwissenschaften, Medizin, Informatik, Technik > Physik, Astronomie > Sonstiges; 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