Machine Learning Based Optimization of Laser-Plasma Accelerators
Springer International Publishing (Verlag)
978-3-031-88082-7 (ISBN)
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.
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.
| Erscheinungsdatum | 11.06.2025 |
|---|---|
| Reihe/Serie | Springer Theses |
| Zusatzinfo | XXXVII, 134 p. 64 illus., 63 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Naturwissenschaften ► Physik / Astronomie ► Plasmaphysik |
| Schlagworte | ANGUS laser system • bayesian optimization • Beam quality optimization • Beam tuning • electron beams • Laser-plasma Interaction • LPA systems • LUX Laser-Plasma Accelerator • Synchrotron Light Sources |
| ISBN-10 | 3-031-88082-X / 303188082X |
| ISBN-13 | 978-3-031-88082-7 / 9783031880827 |
| Zustand | Neuware |
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