Speaker
Description
Atmospheric tomography, the problem of reconstructing atmospheric turbulence profiles from wavefront sensor measurements, is an integral part of
many adaptive optics systems. It is used to enhance the image quality of
ground-based telescopes, such as for the Multiconjugate Adaptive Optics Relay For ELT Observations (MORFEO) instrument on the Extremely Large
Telescope (ELT).
Singular-value and frame decompositions of the underlying atmospheric tomography
operator can reveal useful analytical information on this inverse problem, as well as
serve as the basis of efficient numerical reconstruction algorithms. In this talk, we
extend existing singular value decompositions to more realistic Sobolev settings including
weighted inner products, discuss a frame-based
(approximate) solution operator and focus on the numerical implementation of the SVD-based Atmospheric Tomography with Fourier
Domain Regularization Algorithm (SAFER) and its performance for Multi Conjugate Adaptive Optics (MCAO) systems. The key features of the SAFER
algorithm are the utilization of the FFT and the pre-computation of computationally demanding parts. Together this provides a fast algorithm with
less memory requirements than commonly used Matrix Vector Multiplication
(MVM) approaches. We evaluate the performance of SAFER regarding reconstruction quality and computational expense in numerical experiments using
the Adaptive Optics simulation environment COMPASS.
