Galaxy Light Profile Convolutional Neural Networks (GaLNets). I. Fast and Accurate Structural Parameters for Billion-galaxy Samples

Next-generation large sky surveys will observe up to billions of galaxies for which basic structural parameters are needed to study their evolution. This is a challenging task that, for ground-based observations, is complicated by seeing-limited point-spread functions (PSFs). To perform a fast and accurate analysis of galaxy surface brightness, we have developed a family of supervised convolutional neural networks (CNNs) to derive Sersic profile parameters of galaxies. This work presents the first two Galaxy Light profile CNNs (GaLNets) of this family. The first one is trained using galaxy images only (GaLNet-1), and the second is trained with both galaxy images and the local PSF (GaLNet-2). We have compared the results from GaLNets with structural parameters (total magnitude, effective radius, Sersic index, etc.) derived from a set of galaxies from the Kilo-Degree Survey by 2DPHOT as a representative of the standard PSF-convolved Sersic fitting tools. The comparison shows that GaLNet-2 can reach an accuracy as high as that of 2DPHOT, while GaLNet-1 performs worse because it misses the information from the local PSF. Both GaLNets are three orders of magnitude faster than standard methods in terms of computational speed. This first application of CNNs to ground-based galaxy surface photometry shows that they are promising tools to perform parametric analyses of very large galaxy samples, like the ones expected from the Vera Rubin/LSST surveys. However, GaLNets can be easily modified for space observations from Euclid and the China Space Station Telescope.

Automated summary

This study developed a family of convolutional neural networks (CNNs) to analyze the surface brightness of galaxies quickly and accurately. Two different CNNs were trained to derive Sersic profile parameters, and they showed faster computational speed and comparable accuracy to standard tools in certain cases.