Simultaneous derivation of galaxy physical properties with multimodal deep learning

Abstract

ABSTRACT Upcoming telescopes and surveys will revolutionize our understanding of the Universe by providing unprecedented amounts of observations on extragalactic objects, which will require new tools complementing traditional astronomy methods, in particular machine learning techniques, and above all, deep architectures. In this study, we apply deep learning methods to estimate three essential parameters of galaxy evolution, i.e. redshift, stellar mass, and star formation rate (SFR), from a data set recently analysed and tailored to the Euclid context, containing simulated H-band images and tabulated photometric values. Our approach involved the development of a novel architecture called the FusionNetwork, combining two components suited to the heterogeneous data, ResNet50 for images, and a Multilayer Perceptron (MLP) for tabular data, through an additional MLP providing the overall output. The key achievement of our deep learning approach is the simultaneous estimation of the three quantities, previously estimated separately. Our model outperforms state-of-the-art methods: overall, our best FusionNetwork improves the fraction of correct SFR estimates from ∼70 to ∼80 per cent, while providing comparable results on redshift and stellar mass.