Deep learning prediction of galaxy stellar populations in the low-redshift Universe

Abstract

ABSTRACT The increasing size and complexity of data provided by both ongoing and planned galaxy surveys greatly contribute to our understanding of galaxy evolution. Deep learning methods are particularly well suited for handling the complex and massive data. We train a convolutional neural network (CNN) to simultaneously predict the stellar populations in galaxies: age, metallicity, colour excess E(B − V), and central velocity dispersion (VD) using spectra with redshift ≤ 0.3 from the Sloan Digital Sky Survey. This is the first time to use spectra based on deep learning to derive the four galaxy properties. The testing results show that our CNN predictions of galaxy properties are in good consistent with values by the traditional stellar population synthesis method with little scatters (0.11 dex for age and metallicity, 0.018 mag for E(B − V), and 31 km s−1 for VD). In terms of the computational time, our method reduces by more than 10 times compared to traditional method. We further evaluate the performance of our CNN prediction model using spectra with different signal-to-noise ratios (S/Ns), redshifts, and spectral classes. We find that our model generally exhibits good performance, although the errors at different S/Ns, redshifts, and spectral classes vary slightly. Our well-trained CNN model and related codes are publicly available on https://github.com/sddzwll/CNNforStellarp.