Estimating differential pistons for the Extremely Large Telescope using focal plane imaging and a residual network

Abstract

As the Extremely Large Telescope (ELT) approaches operational status, optimising its imaging performance is critical. A differential piston, arising from either the adaptive optics (AO) control loop, thermomechanical effects, or other sources, significantly degrades the image quality and is detrimental to the telescope's overall performance. In a numerical simulation set-up, we propose a method for estimating the differential piston between the petals of the ELT's M4 mirror using images from a 2x2 Shack-Hartmann wavefront sensor (SH-WFS), commonly used in the ELT's tomographic AO mode. We aim to identify the limitations of this approach by evaluating its sensitivity to various observing conditions and sources of noise. Using a deep learning model based on a ResNet architecture, we trained a neural network (NN) on simulated datasets to estimate the differential piston. We assessed the robustness of the method under various conditions, including variations in Strehl ratio, polychromaticity, and detector noise. The performance was quantified using the root mean square error (RMSE) of the estimated differential piston aberration. This method demonstrates the ability to extract differential piston information from 2x2 SH-WFS images. Temporal averaging of frames makes the differential piston signal emerge from the turbulence-induced speckle field and leads to a significant improvement in the RMSE calculation. As expected, better seeing conditions result in improved accuracy. Polychromaticity only degrades the performance by less than 5% compared to the monochromatic case. In a realistic scenario, detector noise is not a limiting factor, as the primary limitation rather arises from the need for sufficient speckle averaging. The network was also shown to be applicable to input images other than the 2x2 SH-WFS data.

References in corpus (11)

• Fast computation of Lyot-style coronagraph propagation
• On-sky multi-wavelength phasing of segmented telescopes with the Zernike phase contrast sensor
• Fine cophasing of segmented aperture telescopes with ZELDA, a Zernike wavefront sensor in the diffraction-limited regime
• Focal Plane Wavefront Sensing using Machine Learning: Performance of Convolutional Neural Networks compared to Fundamental Limits
• Calibration of the island effect: Experimental validation of closed-loop focal plane wavefront control on Subaru/SCExAO
• The self-coherent camera as a focal plane fine phasing sensor
• A deep learning approach for focal-plane wavefront sensing using vortex phase diversity
• Phasing segmented telescopes via deep learning methods: application to a deployable CubeSat
• High strehl and high contrast for the ELT instrument METIS -- Final design, implementation, and predicted performance of the single-conjugate adaptive optics system
• Laser-guide-stars used for cophasing broad capture ranges
• Strategy for sensing petal mode in presence of AO residual turbulence with pyramid wavefront sensor