Computationally efficient autoregressive method for generating phase screens with frozen flow and turbulence in...

Area of Science:

• Astronomy
• Optics
• Atmospheric Physics

Background:

• Adaptive optics (AO) systems require accurate atmospheric phase screen simulations.
• Existing Fourier-based methods have limitations in periodicity and spatial frequency content.
• Modeling atmospheric turbulence, including
• boiling
• is critical for AO performance.

Purpose of the Study:

• To present a computationally efficient, sample-based autoregressive (AR) method for generating atmospheric phase screens.
• To address limitations of traditional Fourier-based phase screen generation.
• To improve the fidelity of AO simulations by incorporating stochastic atmospheric components.

Main Methods:

• Developed a sample-based, autoregressive (AR) method for phase screen generation and time evolution.
• Introduced a single parameter per Fourier mode to control frozen flow and stochastic components.
• Compared AR phase screens against translating phase screens in AO simulator performance tests.

Main Results:

• The AR method is computationally efficient and overcomes limitations of Fourier-based approaches.
• Simulations with AR phase screens showed significantly elevated residual closed-loop temporal power with added stochastic content.
• Preliminary evidence suggests the AR model provides a better fit to AO telemetry than pure frozen flow models.

Conclusions:

• The proposed AR method offers a more realistic and efficient approach to simulating atmospheric phase screens for AO.
• Accurate modeling of atmospheric
• boiling
• is essential for reliable AO system performance.
• The AR model represents a significant improvement for AO simulation fidelity and real-world condition reflection.