Volume 16, Issue 2 (Summer-Fall 2022)                   IJOP 2022, 16(2): 121-130 | Back to browse issues page

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Bakhtiar Shohani J, Hajimahmoodzadeh M, Fallah H. Double-Star Detection Using Convolutional Neural Network in Atmospheric Turbulence. IJOP 2022; 16 (2) :121-130
URL: http://ijop.ir/article-1-512-en.html
1- Department of Physics, University of Isfahan, Isfahan, Iran
2- Quantum Optics Group, Department of Physics, University of Isfahan, Isfahan, Iran
Abstract:   (1536 Views)
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%.
Full-Text [PDF 566 kb]   (1157 Downloads)    
Type of Study: Research | Subject: Atmospheric Optics and Remote Sensing
Received: 2022/09/25 | Revised: 2023/06/20 | Accepted: 2022/11/22 | Published: 2023/06/19

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