Phot. metallicity prediction of RR Lyrae stars

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 Ks 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 R2 regression performance of 0.84 and 0.93 for the Ks and G bands, respectively, measured by cross-validation.