Continuous Active Correction of Deployable Space Telescopes Using Machine Learning

Abstract

Achieving high-resolution imaging for a deployable space telescope requires segments co-phased to nanometer precision. Typical alignment uses point sources (distant stars) or extended ground scenes. An on-board active correction system has been developed to provide continuous alignment by using a fibre source instead. IMPACT (Image-based Mirror Phasing and Alignment using Convolutional neTworks) measures point spread functions (PSFs) on a secondary mirror detector separated from the main science camera. By applying a deep machine learning algorithm, piston and tip/tilt aberrations can be retrieved and corrected for. When simulated from a uniform distribution, PSF images were corrected from a mean Strehl of 0.18 to 0.99 after two model passes, reducing RMS errors from a mean of 276.7 nm down to 11.1 nm.