  The point spread function reflects the state of an optical telescope and it
is important for data post-processing methods design. 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 to use the 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 pure data based point
spread function modelling method, 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 as
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 can map any star images obtained by wide
field small aperture telescopes directly to its point spread function, which
could be used to design data post-processing or optical system alignment
methods.
