Point spread function estimation for wide field small aperture telescopes with deep neural networks and calibration data

ABSTRACT The point spread function (PSF) reflects states of a telescope and plays an important role in the development data-processing methods, such as PSF-based astrometry, photometry, image restoration. However, for wide field small aperture telescopes (WFSATs), estimating PSF any position whole view is hard, because aberrations induced by optical system are quite complex signal-to-noise ratio star images often too low estimation. In this paper, we further develop our deep neural network (DNN)-based modelling method show its applications During alignment testing stage, collects calibration data through modification elements within engineering tolerances (tilting decentring). Then, use these to train DNN (Tel–Net). After training, Tel–Net can estimate from several discretely sampled images. We both simulated experimental test performance method. results that successfully reconstruct PSFs WFSATs positions (FoV). Its significantly more precise than obtained compared classic – inverse distance weight interpolation. Our provides foundations developing network-based methods WFSATs, which require strong prior information PSFs.