Prediction of wavefronts in adaptive optics to reduce servo lag errors using data mining

TL;DR

This paper presents a data mining approach to predict future wavefronts in adaptive optics, aiming to reduce servo lag errors and improve wavefront correction accuracy.

Contribution

It introduces a novel three-step data mining method for wavefront prediction and demonstrates a 6% improvement in correction accuracy through simulations.

Findings

Achieved 6% improvement in wavefront correction accuracy.

Optimized segment size enhances prediction performance.

Method helps in selecting suitable exposure times for adaptive optics.

Abstract

Servo lag errors in adaptive optics lead to inaccurate compensation of wavefront distortions. An attempt has been made to predict future wavefronts using data mining on wavefronts of the immediate past to reduce these errors. Monte Carlo simulations were performed on experimentally obtained data that closely follows Kolmogorov phase characteristics. An improvement of 6% in wavefront correction is reported after data mining is performed. Data mining is performed in three steps (a) Data cube Segmentation (b) Polynomial Interpolation and (c) Wavefront Estimation. It is important to optimize the segment size that gives best prediction results. Optimization of the best predictable future helps in selecting a suitable exposure time.