# Impact of time-variant turbulence behavior on prediction for adaptive optics systems

## TL;DR

This paper investigates how time-varying atmospheric turbulence, especially fluctuating wind speeds, affects the accuracy of wavefront prediction in adaptive optics systems, highlighting the importance of accounting for turbulence variability.

## Contribution

It introduces a stochastic model for time-variant atmospheric turbulence and evaluates a data-driven predictor's performance under varying wind conditions in AO systems.

## Key findings

- Varying wind significantly impacts wavefront prediction accuracy.
- High wind speeds cause the greatest performance loss.
- Accounting for turbulence variability is crucial for optimal AO performance.

## Abstract

For high contrast imaging systems, the time delay is one of the major limiting factors for the performance of the extreme adaptive optics (AO) sub-system and, in turn, the final contrast. The time delay is due to the finite time needed to measure the incoming disturbance and then apply the correction. By predicting the behavior of the atmospheric disturbance over the time delay we can in principle achieve a better AO performance. Atmospheric turbulence parameters which determine the wavefront phase fluctuations have time-varying behavior. We present a stochastic model for wind speed and model time-variant atmospheric turbulence effects using varying wind speed. We test a low-order, data-driven predictor, the linear minimum mean square error predictor, for a near-infrared AO system under varying conditions. Our results show varying wind can have a significant impact on the performance of wavefront prediction, preventing it from reaching optimal performance. The impact depends on the strength of the wind fluctuations with the greatest loss in expected performance being for high wind speeds.