Atmospheric turbulence profiling with unknown power spectral density

TL;DR

This paper extends the SLODAR method for atmospheric turbulence profiling by incorporating a non-Kolmogorov layer with unknown spectral density, improving accuracy in turbulence reconstruction.

Contribution

It introduces a novel approach to jointly estimate turbulence profiles and unknown spectral densities, addressing limitations of the standard Kolmogorov model.

Findings

Numerical simulations show improved turbulence profile reconstruction.

The methods can accurately detect local perturbations in spectral densities.

Joint estimation is ill-posed, but the proposed methods mitigate this issue.

Abstract

Adaptive optics (AO) is a technology in modern ground-based optical telescopes to compensate the wavefront distortions caused by atmospheric turbulence. One method that allows to retrieve information about the atmosphere from telescope data is so-called SLODAR, where the atmospheric turbulence profile is estimated based on correlation data of Shack--Hartmann wavefront measurements. This approach relies on a layered Kolmogorov turbulence model. In this article, we propose a novel extension of the SLODAR concept by including a general non-Kolmogorov turbulence layer close to the ground with an unknown power spectral density. We prove that the joint estimation problem of the turbulence profile above ground simultaneously with the unknown power spectral density at the ground is ill-posed and propose three numerical reconstruction methods. We demonstrate by numerical simulations that our methods lead to substantial improvements in the turbulence profile reconstruction, compared to standard SLODAR-type approach. Also, our methods can accurately locate local perturbations in non-Kolmogorov power spectral densities.