Double-Star Detection Using Convolutional Neural Network in Atmospheric Turbulence

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Bakhtiar Shohani Jafar; Hajimahmoodzadeh Morteza; Fallah Hamidreza

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Journal Paper

Paper Information

Journal: International Journal of Optics and Photonics Year:2022 | Volume:16 | Issue:2 Page(s): 121-130

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Information Journal Paper

Title

Double-Star Detection Using Convolutional Neural Network in Atmospheric Turbulence

Author(s)

Bakhtiar Shohani Jafar | Hajimahmoodzadeh Morteza | Fallah Hamidreza | Issue Writer Certificate

Keywords

Aberration

Turbulence

Double Stars

Convolutional Neural Network

Machine Learning

Abstract

In this paper, we investigate the usage of Machine Learning in the detection and recognition of Double Stars. To do this, numerous images including one star and Double Stars are simulated. Then, 100 terms of Zernike expansion with random coefficients are considered as Aberrations to impose on the aforementioned images. Also, a telescope with a specific aperture is simulated. In this work, two kinds of intensity are used, one is in-focus and the other is out-of-focus of the telescope. After these simulations, a Convolutional Neural Network (CNN) is configured and designed and its input is simulated intensity patterns. After learning the network, we could recognize Double Stars at severe Turbulence without needing phase correction with a very high accuracy level of more than 98%.

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