Estimating [Fe/H] of RR Lyrae using Gaia DR3

The framework incorporates a rigorous preprocessing pipeline (including phase folding, smoothing, and sample weighting) and is trained using Gaia DR3 G-band light curves and photometric metallicities of RRLs, available in the literature. The model architecture and training implicitly handle the morphological differences between RRab and RRc light curves. Our unified GRU model achieves high predictive accuracy.

It successfully confirms the high precision for RRab stars reported in our previous work (RMSE=0.0765dex, R2=0.9401) and, crucially, demonstrates even stronger performance for the more challenging RRc stars (RMSE=0.0720dex, R2=0.9625). This represents a significant improvement over previous DL benchmarks. We also present a key finding: a clear positive correlation between the number of photometric data points in a light curve and the precision of the final metallicity estimate, quantifying the value of well-sampled observations.

Crucially, we demonstrate that prediction accuracy scales with the number of photometric epochs, establishing that this framework is poised to deliver unprecedented precision with richer future datasets. The enhanced light curves from Gaia DR4 and the Vera C. Rubin Observatory will enable this methodology to produce metallicity catalogues of unprecedented scale and fidelity, paving the way for next-generation studies in Galactic archaeology and chemo-dynamics.