Directly deriving atmospheric parameters for one million stars from SMSS photometric images

The precise determination of stellar atmospheric parameters (effective temperature T eff , surface gravity log g , and metallicity [Fe/H]) serves as a cornerstone of Galactic studies. In this work, we develop a novel deep learning approach, the Atmospheric CSWin Framework (ACF), to measure these parameters with high precision. The ACF employs a dual-input architecture that combines astrometric data (parallaxes and their corresponding errors) from Gaia Early Data Release 3 with photometric images from the fourth data release (DR4) of the SkyMapper Southern Survey (SMSS). The framework utilizes a CSWin Transformer backbone for hierarchical feature extraction from photometric images, integrated with Monte Carlo dropout in the prediction module for robust uncertainty quantification. Trained on cross-matched stars between SMSS DR4 and the third data release of the Galactic Archaeology with HERMES spectroscopic survey, the ACF achieves parameter estimates with dispersions of 95.02 K for T eff , 0.07 dex for log g , and 0.14 dex for [Fe/H]. Systematic experiments demonstrate that incorporating parallax information significantly improves the precision of all three parameters, especially log g . Our image-based methods outperform traditional approaches based on stellar magnitudes or colors, with improvements ranging from 2% to 14%. The ACF yields parameter estimates approaching those of high-resolution spectroscopic analyses, and the framework remains effective even for low-quality samples, highlighting its robustness and general applicability. Using the ACF, we compiled a comprehensive catalog of atmospheric parameters for one million SMSS DR4 stars.