SPT-GloCal: Enhancing [O/Fe] and [Mg/Fe] determinations in metal-poor stars with UV-extended low-resolution CSST spectra

This is the authors' abstract. We don't add key points for this paper.

Metal-poor stars (MP, [Fe/H]<−1.0) retain the chemical signatures of the early Universe, making their α-element invaluable for tracing the Galactic chemical evolution. Traditional optical spectroscopic methods and machine learning approaches often struggle at low metallicities and neglect the diagnostic power of ultraviolet (UV) spectral features (e.g., OH bands, Mg II/Mg I lines) that are accessible to the forthcoming Chinese Space Station Telescope (CSST). Using over 1.8 × 10 5 simulated CSST spectra, we developed the spectral transformer (SPT)-GloCal model to quantify the impact of UV (2550−4000 Å) low-resolution ( R ≈ 200) spectra on the precision of [O/Fe] and [Mg/Fe] estimates in MP stars. The model improves local attention through score-aware competitive filtering and integrates it with global attention via a learnable gating mechanism. We compared models trained on full spectra versus optical-only spectra. Incorporating UV spectra halved the mean absolute error (MAE) of [O/Fe] predictions from 0.0785 to 0.0367 dex and reduced the scatter ( σ ) from 0.135 to 0.063 dex. For [Mg/Fe], MAE decreased from 0.0010 to 0.0006 dex and σ from 0.054 to 0.0068 dex. Error analyses demonstrated that UV data lead to more stable and accurate estimates, especially at a high log g and low Teff. SPT-GloCal outperforms both its predecessor (SPT) and tree-based regressors, confirming the effectiveness of its global-local attention design. Furthermore, tests on spectra with simulated noise show that the model maintains consistent performance across a range of signal-to-noise ratios (S/N =30−50), with marginal gains over the SPT model at the lower S/N end. UV spectral features, even at low resolution ( R ≈ 200), enhance α-element abundance determinations. The SPT-GloCal framework provides a scalable solution for upcoming space-based surveys. Future work will apply this model to real CSST data, thus extending it to other elements.