Nick Murphy, Georgia State University; Vignesh Sathia, Georgia State University; Berkay Aydin, Georgia State University; Dustin Kempton, Georgia State University; Stuart Jefferies, Georgia State University; Fabien Baron, Georgia State University
Keywords: atmospheric turbulence, machine learning, adaptive optics, telescope aperture, speckle image, ground-based telescopes
Abstract:
Ground-based telescopes are used to record extraterrestrial images with great scientific, commercial, and military importance. Yet the images they collect are blurred due to ever-changing conditions and turbulence in the Earth’s atmosphere. Current methods to mitigate this issue include adaptive optics, which measures the current conditions with a wavefront sensor and then deforms a mirror in the telescope’s optical path. This process produces clearer images, but requires precise adjustments which may not scale as the number of mirror segments increases.
In this work, we propose a multi-task learning approach coupled with representation learning to determine monochromatic wavefront aberrations at a single telescope aperture in real time. This approach utilizes shared weights for learning relevant, and inter-related parameters of wind layers, which allows for better scaling and control of multi-segment mirror adaptive optics.
In other words, we seek to uncover aberration-related information about the characteristics of each layer of atmospheric turbulence from the distorted image. To create this model, we first develop a training dataset of 3D rasters – each instance corresponds to a set of speckle images of size NxNxP where the NxN is the size of individual images. These rasters are distorted by a Point Spread Function that simulates the effects of relevant atmospheric conditions. The atmospheric conditions in the form of wind layer velocity vectors will be used to label these instances. This considerably sparse dataset will then be processed using self-supervised learning models to learn representations in sufficiently low dimensions and extract important features. The resulting encoded representations will then be used to estimate the multi-layer wind velocities, where we will utilize multi-task learning approaches.
The proposed model is envisioned to provide the necessary adjustment values for adaptive optics systems, at comparable levels of precision to existing physical wavefront sensors. It can then be used as a tool to manage a large multi-segment mirror adaptive optics system with less reliance on expensive hardware and environmental conditions.
Date of Conference: September 17-20, 2024
Track: Atmospherics/Space Weather