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The point spread function reflects the state of an optical telescope and itis important for data post-processing methods design. For wide field smallaperture telescopes, the point spread function is hard to model, because it isaffected by many different effects and has strong temporal and spatialvariations. In this paper, we propose to use the denoising autoencoder, a typeof deep neural network, to model the point spread function of wide field smallaperture telescopes. The denoising autoencoder is a pure data based pointspread function modelling method, which uses calibration data from realobservations or numerical simulated results as point spread function templates.According to real observation conditions, different levels of random noise oraberrations are added to point spread function templates, making them asrealizations of the point spread function, i.e., simulated star images. Then wetrain the denoising autoencoder with realizations and templates of the pointspread function. After training, the denoising autoencoder learns the manifoldspace of the point spread function and can map any star images obtained by widefield small aperture telescopes directly to its point spread function, whichcould be used to design data post-processing or optical system alignmentmethods.

Point Spread Function Modelling for Wide Field Small Aperture Telescopes with a Denoising Autoencoder