Stray light suppression design of optomechanical systems enabled by deep reinforcement learning

Area of Science:

• Optomechanics
• Artificial Intelligence
• Optical Engineering

Background:

• Stray light suppression is critical for optomechanical systems.
• Current methods are complex, time-consuming, and require significant expertise.
• Investigating stray light suppression poses challenges due to scattered light uncertainty.

Purpose of the Study:

• To validate the feasibility of using reinforcement learning for stray light suppression.
• To develop an efficient method for stray light suppression in optomechanical systems.

Main Methods:

• A model-based deep reinforcement learning approach was employed.
• The method was integrated within a Monte Carlo ray-tracing environment.
• Suppression strategies were devised using this integrated approach.

Main Results:

• The model-based deep reinforcement learning method demonstrated effective stray light suppression.
• The approach proved successful across various optical system configurations.
• Significant improvements in stray light suppression efficiency were achieved.

Conclusions:

• Reinforcement learning is a feasible and effective tool for stray light suppression in optomechanical systems.
• This AI-driven method offers a more efficient alternative to traditional techniques.
• The study highlights the potential of AI in optimizing optical system performance.