DeepForge for astronomy: Deep learning SDSS redshifts from images

Abstract

Dedicated survey telescopes provide multicolor imaging observations every night, whose scientific impact hinges on the subsequent data analysis. Deep learning has recently provided breakthroughs in many areas of astronomy due to its ability to extract optimal features from the input data during training. Photometric redshift estimation in particular seems like a perfect application. Directly analyzing the images instead of traditional summary catalogs holds the promise of naturally combining spectral and morphological information. Here we introduce a complete solution called DeepForge for creating, training, and running deep networks for astronomical analyses using a convenient web portal and Python. Following the approach by Pasquet et al. (2019) we demonstrate unprecedented accuracy on the Sloan Digital Sky Survey measurements. We study variants of their network and analyze the optimal feature space using low-dimensional embedding, which clearly reveal the power of the custom features. With DeepForge the process of experimentation is straightforward and has the potential to further improve the current state of the art in photometric redshift estimation.