Catalog of stellar atmospheric parameters

This work aims to develop a novel deep learning-based 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, ACF achieves parameter estimates with dispersions of 95.02K for T_eff_, 0.07dex for logg, and 0.14dex for [Fe/H].