Improving position accuracy for telescopes with small aperture and wide field of view utilizing point spread function modelling

2020 ◽  
Vol 497 (3) ◽  
pp. 4000-4008
Author(s):  
Rongyu Sun ◽  
Shengxian Yu ◽  
Peng Jia ◽  
Changyin Zhao

ABSTRACT Telescopes with a small aperture and a wide field of view are widely used and play a significant role in large-scale state-of-the-art sky survey applications, such as transient detection and near-Earth object observations. However, owing to the specific defects caused by optical aberrations, the image quality and efficiency of source detection are affected. To achieve high-accuracy position measurements, an innovative technique is proposed. First, a large number of raw images are analysed using principal component analysis. Then, the effective point spread function is reconstructed, which reflects the state of the telescope and reveals the characteristics of the imaging process. Finally, based on the point spread function model, the centroids of star images are estimated iteratively. To test the efficiency and reliability of our algorithm, a large number of simulated images are produced, and a telescope with small aperture and wide field of view is utilized to acquire the raw images. The position measurement of sources is performed using our novel method and two other common methods on these data. Based on a comparison of the results, the improvement is investigated, and it is demonstrated that our proposed technique outperforms the others on position accuracy. We explore the limitations and potential gains that may be achieved by applying this technique to custom systems designed specifically for wide-field astronomical applications.

2020 ◽  
Vol 493 (1) ◽  
pp. 651-660 ◽  
Author(s):  
Peng Jia ◽  
Xiyu Li ◽  
Zhengyang Li ◽  
Weinan Wang ◽  
Dongmei Cai

ABSTRACT The point spread function reflects the state of an optical telescope and it is important for the design of data post-processing methods. For wide-field small-aperture telescopes, the point spread function is hard to model because it is affected by many different effects and has strong temporal and spatial variations. In this paper, we propose the use of a denoising autoencoder, a type of deep neural network, to model the point spread function of wide-field small-aperture telescopes. The denoising autoencoder is a point spread function modelling method, based on pure data, which uses calibration data from real observations or numerical simulated results as point spread function templates. According to real observation conditions, different levels of random noise or aberrations are added to point spread function templates, making them realizations of the point spread function (i.e. simulated star images). Then we train the denoising autoencoder with realizations and templates of the point spread function. After training, the denoising autoencoder learns the manifold space of the point spread function and it can map any star images obtained by wide-field small-aperture telescopes directly to its point spread function. This could be used to design data post-processing or optical system alignment methods.


2008 ◽  
Vol 60 (sp1) ◽  
pp. S35-S41 ◽  
Author(s):  
Yasunobu Uchiyama ◽  
Yoshitomo Maeda ◽  
Masatoshi Ebara ◽  
Ryuichi Fujimoto ◽  
Yoshitaka Ishisaki ◽  
...  

2020 ◽  
Vol 59 (23) ◽  
pp. 7114 ◽  
Author(s):  
Wu Qiong ◽  
Kun Gan ◽  
Zizheng Hua ◽  
Zhenzhou Zhang ◽  
Hanwen Zhao ◽  
...  

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