Predicted redshifts of galaxies with NetZ

Since spectroscopic redshifts require additional telescope and human resources, millions of galaxies are known without spectroscopic redshifts. Therefore, it is crucial to have methods for estimating the redshift of a galaxy based on its photometric properties, the so-called photo-z.

We developed NetZ, a new method using a Convolutional Neural Network (CNN) to predict the photo-z based on galaxy images, in contrast to previous methods which often used only the integrated photometries of galaxies without their images. We use data from the Hyper Suprime-Cam Subaru Strategic Program (HSC SSP) in five different filters as training data. The network over the whole redshift range between 0 and 4 performs well overall and especially in the high-z range better than other methods on the same data.