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

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Transient plasma photonic structures offer a compact solution for high-power laser optics. Machine learning rapidly designs these robust plasma mirrors, enabling new laser applications and discovering pulse compression.

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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.