Neural Network Based Point Spread Function Deconvolution For Astronomical Applications

Abstract

Optical astronomical images are strongly affected by the point spread function (PSF) of the optical system and the atmosphere (seeing) which blurs the observed image. The amount of blurring depends both on the observed band, and more crucially, on the atmospheric conditions during observation. A typical astronomical image will therefore have a unique PSF that is non-circular and different in different bands. Observations of known stars give us a determination of this PSF. Therefore, any serious candidate for production analysis of astronomical images must take the known PSF into account during the image analysis. So far the majority of applications of neural networks (NN) to astronomical image analysis have ignored this problem by assuming a fixed PSF in training and validation. We present a neural network based deconvolution algorithm based on Deep Wiener Deconvolution Network (DWDN) that takes the PSF shape into account when performing deconvolution as an example of one possible approach to enabling neural network to use the PSF information. We study the performance of several versions of this algorithm under realistic observational conditions in terms of recovery of most relevant astronomical quantities such as colors, ellipticities and orientations. We also investigate the performance of custom loss functions and find that they cause modest improvements in the recovery of astronomical quantities.Comment: 12 pages, 6 figure