Photometric Metallicity Prediction of Fundamental-mode RR Lyrae Stars in the Gaia Optical and K s Infrared Wave Bands by Deep Learning

RR Lyrae stars are useful chemical tracers thanks to the empirical relationship between their heavy-element abundance and the shape of their light curves. However, the consistent and accurate calibration of this relation across multiple photometric wave bands has been lacking. We have devised a new method for the metallicity estimation of fundamental-mode RR Lyrae stars in the Gaia optical G and near-infrared VISTA K ( s ) wave bands by deep learning. First, an existing metallicity prediction method is applied to large photometric data sets, which are then used to train long short-term memory recurrent neural networks for the regression of the [Fe/H] to the light curves in other wave bands. This approach allows an unbiased transfer of our accurate, spectroscopically calibrated I-band formula to additional bands at the expense of minimal additional noise. We achieve a low mean absolute error of 0.1 dex and high R (2) regression performance of 0.84 and 0.93 for the K ( s ) and G bands, respectively, measured by cross-validation. The resulting predictive models are deployed on the Gaia DR2 and VVV inner bulge RR Lyrae catalogs. We estimate mean metallicities of -1.35 dex for the inner bulge and -1.7 dex for the halo, which are significantly less than the values obtained by earlier photometric prediction methods. Using our results, we establish a public catalog of photometric metallicities of over 60,000 Galactic RR Lyrae stars and provide an all-sky map of the resulting RR Lyrae metallicity distribution. The software code used for training and deploying our recurrent neural networks is made publicly available in the open-source domain.