Using convolutional neural networks to predict galaxy metallicity from three-colour images. Issue 4 (1st February 2019) - Wu, John F; Boada, Steven
Abstract: We train a deep residual convolutional neural network (CNN) to predict the gas-phase metallicity ( Z ) of galaxies derived from spectroscopic information (Z 12 + (O/H) ) using only three-band gri images from the Sloan Digital Sky Survey. When trained and tested on 128 × 128-pixel images, the root mean squared error (RMSE) of Z pred − Z true is only 0.085 dex, vastly outperforming a trained random forest algorithm on the same data set (RMSE = 0.130 dex). The amount of scatter in Z pred − Z true decreases with increasing image resolution in an intuitive manner. We are able to use CNN-predicted Z pred and independently measured stellar masses to recover a mass–metallicity relation with 0.10 dex scatter. Because our predicted MZR shows no more scatter than the empirical MZR, the difference between Z pred and Z true cannot be due to purely random error. This suggests that the CNN has learned a representation of the gas-phase metallicity, from the optical imaging, beyond what is accessible with oxygen spectral lines.