A computationally efficient autoregressive method for generating phase screens with frozen flow and turbulence in optical simulations

TL;DR

This paper introduces a computationally efficient autoregressive method for generating atmospheric phase screens that better models turbulence and frozen flow in optical simulations, improving realism over traditional Fourier-based approaches.

Contribution

The paper presents a novel autoregressive approach for phase screen generation that addresses Fourier method limitations and incorporates atmospheric boiling effects.

Findings

AR phase screens improve AO simulation realism

Model fits to AO telemetry outperform frozen flow assumptions

Increased stochastic content enhances temporal power modeling

Abstract

We present a sample-based, autoregressive (AR) method for the generation and time evolution of atmospheric phase screens that is computationally efficient and uses a single parameter per Fourier mode to vary the power contained in the frozen flow and stochastic components. We address limitations of Fourier-based methods such as screen periodicity and low spatial frequency power content. Comparisons of adaptive optics (AO) simulator performance when fed AR phase screens and translating phase screens reveal significantly elevated residual closed-loop temporal power for small increases in added stochastic content at each time step, thus displaying the importance of properly modeling atmospheric "boiling". We present preliminary evidence that our model fits to AO telemetry are better reflections of real conditions than the pure frozen flow assumption.