Abstract
Unlike supervised machine learning methods, reinforcement learning allows an entity to learn how to deploy a task from experience rather than labeled data. This approach has been used in this paper to correct piston misalignment between segments in a segmented mirror telescope. It was proven in simulations that the algorithm converges to a point where it learns how to move the piston actuators in order to maximize the Strehl ratio of the wavefront at the intersection.
Highlights
It is desirable to shorten the observation time needed by a terrestrial telescope to obtain a certain signal-to-noise ratio
Successful construction of telescopes of 8 m and larger has been possible to a large extent with the introduction of segmented mirrors
The electromagnetic field emitted by a distant point source of light such as a star reaches the pupil of the telescope in the form of a plane wave
Summary
It is desirable to shorten the observation time needed by a terrestrial telescope to obtain a certain signal-to-noise ratio. The ones that are most currently used are based on Shack–Hartman wavefront sensors [1,2] These methods rely on intensity images measured at the pupil plane. As far as we are concerned, all machine learning applications to piston sensing so far stand on the supervised paradigm [7,8,9] In this setting, input data and target have to be supplied. In the optical phasing problem, real telescope diffraction images might be available They lack the exact piston values that gave rise to those images. The RL algorithm learns in place with data provided by the telescope mirror in real time It relies on an external physical quantity rather than labels. The method employs a convolutional neural network that takes as input an intensity image measured at an intermediate plane with four different wavelengths. Conclusions and final remarks are found at the end of the paper
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