arXiv subjects

Alexandra Kupersmith

Publications and source records attributed to Alexandra Kupersmith.

2 recordsLinked to original sources

arXiv2026

Tiny Observatory for Telescope Optimization (TOTO): testing algorithms for autonomous on-orbit alignment for space-based telescope systems

The Tiny Observatory for Telescope Optimization (TOTO) is an optical testbed designed to evaluate the efficacy of autonomously driven alignment algorithms for space-based telescope systems. For space-based missions, active control of the telescope alignment on-orbit offers potential to relax passive alignment requirements and reduce on-ground verification activities. TOTO is used to evaluate and verify simulation work of two primary alignment algorithms, Stochastic Parallel Gradient Descent (SPGD) and focus-diverse phase retrieval (FDPR). Previous simulation work has confirmed that by using SPGD for coarse alignment followed by focus-diverse phase retrieval for fine alignment, we can reach diffraction-limited performance on-orbit. This paper presents the results of the autonomous alignment algorithm of a Cassegrain telescope using TOTO. We report the current status of TOTO as well as preliminary results from SPGD and phase retrieval on the testbed using monochromatic light source to simulate an on-axis point source.

astro-ph.IM↗

arXiv2026

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

Phase retrieval techniques are utilized to correct low order wavefront 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 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 wavefront sensing performance of the Machine Learning model with known truth values of the generated dataset. The model predictions for low order Zernikes on TOTO data after training and validation show a reasonable agreement with the true Zernike coefficients.

astro-ph.IM↗