Pareto Optimization and Tuning of a Laser Wakefield Accelerator.

Area of Science:

• Physics
• Accelerator Science
• Computational Physics

Background:

• Optimizing accelerator performance involves complex trade-offs, especially in unknown systems.
• Balancing multiple optimization goals for systems like laser wakefield accelerators (LWFAs) is challenging.

Purpose of the Study:

• To demonstrate the efficacy of multiobjective Bayesian optimization for mapping the solution space of LWFAs.
• To achieve accurate energy tuning of LWFAs and develop an inverse model for user-specified beam parameters.

Main Methods:

• Utilized multiobjective Bayesian optimization to explore the parameter space of an LWFA.
• Employed Gaussian process modeling for forward prediction and developed an inverse model for parameter retrieval.
• Simultaneously adjusted eight parameters to tune LWFA energy.

Main Results:

• Identified a wide range of Pareto-optimal solutions trading beam energy against charge at consistent laser-to-beam efficiency.
• Successfully demonstrated accurate energy tuning of the LWFA from 150 to 400 MeV.
• Developed an inverse model capable of generating input parameter ranges for desired beam characteristics.

Conclusions:

• Multiobjective Bayesian optimization offers a sample-efficient approach to LWFA characterization and tuning.
• The proposed inverse model significantly facilitates LWFA operation by enabling user-defined parameter settings.
• This methodology is expected to accelerate the practical application and development of LWFAs.