Testing of machine learning phase retrieval algorithms on the Tiny Observatory for Telescope Optimization (TOTO) testbed

Phase retrieval techniques are utilized to correct low order aberrations originating from misalignments of the optical system in space based telescope concepts. Traditional phase retrieval involves observation of the Point Spread Function (PSF) and a diversity measurement, usually focus diversity although other measures are possible, to reconstruct the incident wavefront at the science detector. We consider a Machine Learning Phase Retrieval model trained originally on simulated data, and then augmented with real focus diversity data from the Tiny Observatory for Telescope Optimization (TOTO) testbed at the University of Arizona. We then compare the performance of the Machine Learning Phase Retrieval method with a traditional phase retrieval technique and assess the difference in performance.