Papers › Using convolutional neural networks to predict galaxy metallicity from three-color images
Using convolutional neural networks to predict galaxy metallicity from three-color images
John F. Wu, Steven Boada
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We train a deep residual convolutional neural network (CNN) to predict the gas-phase metallicity (Z) of galaxies derived from spectroscopic information (Z ≡12 + log(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) - Zₜᵣᵤₑ 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) - Zₜᵣᵤₑ 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 Zₜᵣᵤₑ can not 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.
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