The Stellar Abundances and Galactic Evolution Survey (SAGES). IV. Surface Gravity Estimation and Giant–Dwarf Separation with the DDO51 Filter

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Abstract Reliable estimation of stellar surface gravity (log g ) for a large sample is crucial for evaluating stellar evolution models and understanding galactic structure; however, it is not easy to accomplish due to the difficulty in gathering a large spectroscopic data set. A photometric sky survey using a specific filter, on the other hand, can play a substantial role in the assessment of log g . The Stellar Abundances and Galactic Evolution Survey (SAGES) utilizes eight filters to provide accurate stellar parameters for ∼10 7 stars, with its DDO51 intermediate-band filter specifically designed for robust log g determination. In this work, the observed SAGES u SC and v SAGES photometry and the synthetic photometry in the g , r , i , and DDO51 bands derived from Gaia XP spectra are employed to investigate the importance of the DDO51 filter in the determination of log g . We applied machine learning-based extinction correction and employed XGBoost models, trained on stellar parameters from Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), to predict log g using photometric data. By comparing the model-predicted log g with LAMOST values, we find that including the DDO51 filter improves the accuracy of log g estimates by 21.0% (from 0.224 to 0.177 dex) overall and by 26.5% (from 0.302 to 0.222 dex) for G- and K-type stars as compared to those obtained without DDO51. The DDO51 filter is also validated to be particularly effective for metal-poor stars ([Fe/H] < −1.0), where it significantly mitigates systematic biases. Our findings highlight the diagnostic power of the SAGES DDO51 filter, providing enhanced stellar characterization vital for future in-depth studies of the Milky Way.

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