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Single step phase optimisation for coherent beam combination using deep learning.

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Summary
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A neural network rapidly identifies fiber phases for coherent beam combination, enabling precise control and bespoke beam shaping. This deep learning approach overcomes limitations in complex fiber laser systems.

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Area of Science:

  • Optics and Photonics
  • Computational Physics
  • Laser Technology

Background:

  • Coherent beam combination (CBC) of multiple fibers enhances power handling beyond single-fiber limits.
  • Precise phase control of individual fiber channels is crucial for CBC, but phase information is often obfuscated in large systems.
  • Current phase retrieval methods are often iterative and too slow for real-time applications, especially with phase noise in fiber laser systems.

Purpose of the Study:

  • To develop a rapid, single-step phase retrieval method for coherent beam combination using artificial intelligence.
  • To demonstrate the capability of a neural network for precise phase identification and subsequent beam shaping.
  • To assess the feasibility of using deep learning to determine the physical possibility of desired intensity profiles and its resilience to noise.

Main Methods:

  • A neural network was trained to identify the relative phases of 19 fibers in a simulated hexagonal close-packed arrangement from their focal intensity profile.
  • The trained neural network performed phase identification in a single computational step.
  • Deep learning models were also used to predict the physical possibility of target intensity profiles and evaluate noise resilience.

Main Results:

  • The neural network successfully identified fiber phases from the focal intensity profile in approximately 10 milliseconds.
  • This rapid phase identification enabled bespoke beam shaping for the simulated 19-fiber system.
  • Deep learning accurately determined the feasibility of desired intensity profiles and showed resilience to simulated experimental noise.

Conclusions:

  • Deep learning offers a powerful, fast, and robust solution for phase retrieval in coherent beam combination.
  • The demonstrated single-step neural network approach significantly advances the practical application of multi-fiber laser systems.
  • This work highlights the strong potential of AI for advanced optical beam control and shaping.