Deep Learning Correction Algorithm for The Active Optics System.

Wenxiang Li,Jinbiao Zhao,Jinpeng Li,Xiaojun Zhou,Hengrui Guan,Chao Kang,Shen Huang

doi:10.3390/s20216403

Abstract

The correction of wavefront aberration plays a vital role in active optics. The traditional correction algorithms based on the deformation of the mirror cannot effectively deal with disturbances in the real system. In this study, a new algorithm called deep learning correction algorithm (DLCA) is proposed to compensate for wavefront aberrations and improve the correction capability. The DLCA consists of an actor network and a strategy unit. The actor network is utilized to establish the mapping of active optics systems with disturbances and provide a search basis for the strategy unit, which can increase the search speed; The strategy unit is used to optimize the correction force, which can improve the accuracy of the DLCA. Notably, a heuristic search algorithm is applied to reduce the search time in the strategy unit. The simulation results show that the DLCA can effectively improve correction capability and has good adaptability. Compared with the least square algorithm (LSA), the algorithm we proposed has better performance, indicating that the DLCA is more accurate and can be used in active optics. Moreover, the proposed approach can provide a new idea for further research of active optics.

Highlights

Active optics, a key technique in large modern telescopes, is applied to correct wavefront aberrations and reduce the influence of the primary mirror’s deformations on the beam quality [1].Nowadays, active optics technology is widely utilized in the large modern telescope: The 8 m classVLT (Very Large Telescope) [2], the VISTA (Visible and Infrared Telescope for Astronomy) [3], and LSST (Large Synoptic Survey Telescope) [4].The wavefront aberration of the telescope comes from multiple sources: atmospheric turbulence, manufacturing and assembly errors, and gravity deformation
The atmospheric wavefront aberrations are usually corrected by adaptive optics (AO), while the gravitational wavefront aberrations are corrected by active optics
A deep learning correction algorithm is proposed in this paper for the correction of wavefront aberrations, which is based on the data rather than the mirror’s deformation

Summary

Introduction

A key technique in large modern telescopes, is applied to correct wavefront aberrations and reduce the influence of the primary mirror’s deformations on the beam quality [1].Nowadays, active optics technology is widely utilized in the large modern telescope: The 8 m classVLT (Very Large Telescope) [2], the VISTA (Visible and Infrared Telescope for Astronomy) [3], and LSST (Large Synoptic Survey Telescope) [4].The wavefront aberration of the telescope comes from multiple sources: atmospheric turbulence, manufacturing and assembly errors, and gravity deformation. A key technique in large modern telescopes, is applied to correct wavefront aberrations and reduce the influence of the primary mirror’s deformations on the beam quality [1]. Active optics technology is widely utilized in the large modern telescope: The 8 m class. The wavefront aberration of the telescope comes from multiple sources: atmospheric turbulence, manufacturing and assembly errors, and gravity deformation. The atmospheric wavefront aberrations are usually corrected by adaptive optics (AO), while the gravitational wavefront aberrations are corrected by active optics. Active optics detects the wavefront aberrations by the wavefront detection device such as the interferometer, the Shack–Hartmann sensor, and so on. The active optics can be used in telescopes and in the large-aperture standard mirror for optical detection. The large-aperture standard mirrors using active optics can work in different conditions. The method to Sensors 2020, 20, 6403; doi:10.3390/s20216403 www.mdpi.com/journal/sensors

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