Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit

Similarly, the observation is limited in angular resolution by the resolving power of the telescope (R λ/D) and, if taken from the ground, by the distortions caused by the moving atmosphere (the "seeing"). The total blurring introduced by the combination of the telescope and the atmosphere is described by the point spread function (PSF). An image taken by a telescope can therefore be thought of as a convolution of the true light distribution with this point spread function plus the addition of various sources of noise. The Shannon-Nyquist sampling theorem (Nyquist 1928; Shannon 1949) limits the ability of deconvolution techniques in removing the effect of the PSF, particularly in the presence of noise (Magain et al. 1998; Courbin 1999; Starck et al. 2002). Deconvolution has long been known as an "ill-posed" ABSTRACT Observations of astrophysical objects such as galaxies are limited by various sources of random and systematic noise from the sky background, the optical system of the telescope and the detector used to record the data. Conventional deconvolution techniques are limited in their ability to recover features in imaging data by the Shannon-Nyquist sampling theorem. Here we train a generative adversarial network (GAN) on a sample of 4, 550 images of nearby galaxies at 0.01 z 0.02 from the Sloan Digital Sky Survey and conduct 10 cross validation to evaluate the results. We present a method using a GAN trained on galaxy images that can recover features from artificially degraded images with worse seeing and higher noise than the original with a performance which far exceeds simple deconvolution. The ability to better recover detailed features such as galaxy morphology from low-signal-to-noise and low angular resolution imaging data significantly increases our ability to study existing data sets of astrophysical objects as well as future observations with observatories such as the Large Synoptic Sky Telescope (LSST) and the Hubble and James Webb space telescopes.