Multichannel left-subtract-right feature vector piston error detection method based on a convolutional neural network.

To realize the large-scale and high-precision co-phasing adjustment of synthetic-aperture telescopes, we propose a multichannel left-subtract-right feature vector piston error detection method based on a convolutional neural network, which inherits the high precision and strong noise resistance of the DFA-LSR method while achieving a detection range of (-139λ, 139λ) (λ = 720 nm). In addition, a scheme to build large training datasets was proposed to solve the difficulty in collecting datasets using traditional neural network methods. Finally, simulations verified that this method can guarantee at least 94.96% accuracy with large samples, obtaining a root mean square error of 10.2 nm when the signal-to-noise ratio is 15.

Improved two-step optimization procedure used for designing an apodizer and Lyot stop in the Lyot coronagraph.

The Lyot coronagraph is a widely known astronomical instrument used to realize direct imaging of exoplanets, and designing transmittance of an apodizer and Lyot stop is the key to obtaining high-contrast imaging. In this paper a new (to the best of our knowledge) optimization procedure used to design the apodizer and Lyot stop in the Lyot coronagraph is proposed. A two-step optimization program is established to obtain the optimum transmittance of an apodizer and Lyot stop in a sequential way. By using the optimized apodizer and Lyot stop obtained through the proposed optimization procedure, both the stellar light and its diffraction light could be strongly suppressed. Numerical results indicate that such an optimized Lyot coronagraph can produce a 1e-10 extinction of the stellar light near the diffraction limit (1.59λ/D), and a high contrast imaging of 1e-07 could still be obtained even with the influence of light intensity of planets themselves. In addition, the two-step optimization procedure brings in two benefits. First, the two-step optimization is approximately 1000 times faster than the joint optimization method [J. Astron. Telesc. Instrum. Syst.2, 011012 (2016)2329-412410.1117/1.JATIS.2.1.011012]. Second, the optimum transmittance of the Lyot stop is binary, and therefore, the requirements of the production process are reduced, resulting in a greatly reduced cost. At the same time, the performance of the optimized Lyot coronagraph is also analyzed in the case of a monochromatic light incident and bandwidth light incident, and the effect of the diameter of the Lyot stop on the results is also discussed in this paper, which makes sense when designing a coronagraph.