Design of transient plasma photonic structure mirrors for high-power lasers using deep kernel Bayesian optimisation

Area of Science:

• High-field physics
• Fusion energy research
• Laser-plasma interactions

Background:

• Conventional optical components limit ultra-high power laser development due to damage thresholds.
• Designing large, high-power optical elements is challenging and often impractical.

Purpose of the Study:

• To investigate transient plasma photonic structures as compact and robust reflective elements for high-power lasers.
• To demonstrate the application of machine learning in designing these complex plasma optical components.

Main Methods:

• Formation of transient plasma photonic structures via intercepting laser pulses in gas.
• Utilizing machine learning to explore the complex parameter space for designing plasma mirrors.
• Analyzing the emergent properties of these plasma structures, including reflectivity and pulse compression.

Main Results:

• Transient plasma photonic structures function as compact, robust reflective elements.
• Machine learning efficiently designed high-reflectivity plasma mirrors.
• A novel regime was discovered where unchirped laser pulses were compressed.

Conclusions:

• Machine learning is a powerful tool for designing advanced optical components for next-generation lasers.
• Transient plasma photonic structures offer a pathway to overcome limitations in high-power laser optics.
• This approach facilitates the development of ultra-compact optical solutions for demanding scientific applications.