Use of Artificial Neural Networks for Hartman Sensor Lenslet Centroid Estimation

Abstract

The resolution of ground based telescopes is limited by the random wavefront aberrations caused by atmospheric turbulence [1]. Adaptive optics systems, which compensate for atmospheric effects, have been shown to improve the resolution of these telescopes [2]. The purpose of an adaptive optics system is to remove the atmospheric induced aberrations from an incident optical wavefront. This removal is accomplished by measuring the incident aberrations and removing them using a deformable mirror. The compensation is degraded by the effects of additive noise in the wavefront sensor (WFS), system time delays, and the possibility of a spatial separation between the object of interest and the beacon used to measure the incident wavefront. While an optimal wavefront reconstruction algorithm, such as the minimum variance reconstructor, can be derived based on statistical knowledge of the atmosphere, noise and other random effects in the adaptive optics system cause the actual performance of this reconstructor to be limited by imperfect knowledge of several key parameters [3, 4, 5]. These parameters include both atmospheric and system parameters. The key atmospheric parameters include the Fried coherence length [6], r0, the C n 2 profile, and the wind speed profile. The key system parameter is the WFS mean square slope measurement error. The most common form of WFS used in adaptive optics is the Hartmann WFS. In this paper we look at reducing the slope measurement error in a Hartmann WFS by improving the subaperture centroid estimation. In particular, neural networks are trained to estimate the subaperture centroid location, and the results are compared to those obtained using a conventional centroid estimator.