scholarly journals On point spread function modelling: towards optimal interpolation

2011 ◽  
Vol 419 (3) ◽  
pp. 2356-2368 ◽  
Author(s):  
Joel Bergé ◽  
Sedona Price ◽  
Adam Amara ◽  
Jason Rhodes
2015 ◽  
Vol 31 (8) ◽  
pp. 948-955 ◽  
Author(s):  
Elena Prieto ◽  
Josep M. Martí-Climent ◽  
Verónica Morán ◽  
Lidia Sancho ◽  
Benigno Barbés ◽  
...  

2020 ◽  
Vol 493 (3) ◽  
pp. 3825-3837
Author(s):  
Dezi Liu ◽  
Wenqiang Deng ◽  
Zuhui Fan ◽  
Liping Fu ◽  
Giovanni Covone ◽  
...  

ABSTRACT The VLT Survey Telescope (VST) Optical Imaging of the CDFS and ES1 Fields Survey, in synergy with the SUDARE survey, is a deep optical ugri imaging of the CDFS and ES1 fields using the VST. The observations for the CDFS field comprise about 4.38 deg2 down to r ∼ 26 mag. The total on-sky time spans over 4 yr in this field, distributed over four adjacent sub-fields. In this paper, we use the multiepoch r-band imaging data to measure the variability of the detected objects and search for transients. We perform careful astrometric and photometric calibrations and point spread function modelling. A new method, referring to as differential running-average photometry, is proposed to measure the light curves of the detected objects. With the method, the difference of PSFs between different epochs can be reduced, and the background fluctuations are also suppressed. Detailed uncertainty analysis and detrending corrections on the light curves are performed. We visually inspect the light curves to select variable objects, and present some objects with interesting light curves. Further investigation of these objects in combination with multiband data will be presented in our forthcoming paper.


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.


Author(s):  
Jia Peng ◽  
Mingyang Ma ◽  
Miao Zhang ◽  
Xuebo Wu ◽  
Weihua Wang ◽  
...  

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