Stellar Abundances & Galactic Evolution Survey

• SAGES is a comprehensive multi-band photometric survey designed to determine key stellar parameters like effective temperature, surface gravity, and metallicity.
• It utilizes uniquely engineered intermediate/narrow-band filters combined with advanced machine learning to achieve high precision calibration and robust parameter estimation.
• The survey’s extensive data release aids in mapping Galactic chemical distribution, identifying rare metal-poor stars, and informing follow-up spectroscopic studies.

The Stellar Abundances and Galactic Evolution Survey (SAGES) is a multi-band, high-precision photometric sky survey designed for the robust determination of stellar atmospheric parameters—most notably effective temperature, surface gravity, and metallicity—across the northern sky. Its architecture, which incorporates a combination of uniquely engineered intermediate- and narrow-band filters (notably uₛ and vₛ) and broad-band SDSS-like photometry, enables detailed studies of Galactic structure, stellar chemical evolution, and the identification of rare stellar populations such as extremely metal-poor stars. SAGES achieves its scientific objectives by integrating observations from several observatories, advanced calibration pipelines, and modern machine-learning–based parameter estimation, with internal and external accuracy benchmarks competitive with state-of-the-art low-resolution spectroscopic surveys (Fan et al., 2023, Huang et al., 2023, Gu et al., 5 Feb 2025, Zhang et al., 18 Sep 2025, Li et al., 14 Oct 2024, Xiao et al., 2023).

1. Survey Design, Architecture, and Filter System

SAGES is conceived as a northern hemisphere photometric survey targeting ≳12,000 deg², deliberately omitting the crowded Galactic plane (typically |b| < 10°) and optimizing for atmospheric parameter determinations at magnitudes as faint as V ~ 20. The core passband system consists of eight filters:

• uₛ: Sensitive to the Balmer jump (gravity), analogous to the Strömgren u band.
• vₛ: Centered on the Ca II H&K doublet (3933/3968 Å), uniquely designed to be ~150 Å bluer than the standard Strömgren v; highly sensitive to metallicity.
• SDSS g, r, i: Allow tight constraints on Tₑff and color indices.
• DDO51: Narrow-band (centered at 5130 Å, Δλ = 162 Å) capturing Mg I b triplet features, crucial for surface gravity (log g) diagnostics.
• Hαₙ, Hα_w: Narrow Hα filters for improved extinction and stellar activity diagnostics (Zheng et al., 2018, Zhang et al., 18 Sep 2025).

Observational data are primarily acquired from the 2.3-m Bok telescope (uₛ, vₛ), the Nanshan One-meter Wide-field Telescope (g, r, i), Zeiss/Maidanak (Hα), and other facilities for DDO51 imaging (Fan et al., 2023, Li et al., 14 Oct 2024). Overlaps of ~20% in field tiling ensure both photometric cross-calibration and spatial homogeneity.

2. Data Acquisition, Processing, and Photometric Calibration

The backbone of SAGES data quality is rooted in high-throughput pipelines and multistage calibration:

• Image Reduction: Includes bias/overscan correction, twilight flat-fielding, cosmic-ray removal, and striping correction. Source extraction leverages the Source Extractor (SE) and is calibrated astrometrically with SCAMP, typically delivering global positional RMS < 0.2–0.3 arcsec relative to Gaia (Fan et al., 2023, Xiao et al., 2023, Li et al., 14 Oct 2024).
• Photometric Calibration: Relative and absolute calibration combine spectroscopy-based Stellar Color Regression (SCR), photometric-based SCR′, and Ubercal methods. Zero-point corrections employ polynomial fitting as a function of CCD position (X,Y) and observation epoch, and are tied to external references (Pan-STARRS1, Gaia BP/RP synthetic photometry). Internal repeatability and uniformity reach 1–2 mmag over ≥1.3° spatial scales, breaking the traditional 1% bottleneck for ground-based surveys (Xiao et al., 2023).
• Depth and Completeness: Single-visit completeness is uₛ ~ 20.4 mag, vₛ ~ 20.3 mag, and g ~ 19.2 mag at S/N ≃ 5; at S/N = 100, limits are typically uₛ ~ 17 mag and vₛ ~ 18 mag (Fan et al., 2023, Li et al., 14 Oct 2024).

3. Stellar Parameter Estimation: Machine Learning and Synthetic Photometry

SAGES deploys advanced machine learning methods to infer stellar parameters from multi-band photometry:

• Random Forest Regression: Used for Tₑff, log g, and [Fe/H] estimation, trained on large spectroscopic samples (e.g., LAMOST, APOGEE, PASTEL) matched to SAGES + Gaia data. This yields precisions of 0.09 dex ([Fe/H]), 0.12 dex (log g), and 70 K (Tₑff), with robust cross-validation and separate models for dwarfs and giants to control systematics (Gu et al., 5 Feb 2025).
• XGBoost Regression with DDO51: The inclusion of DDO51 photometry in the model reduces global log g uncertainties by ∼21% (from 0.224 dex to 0.177 dex), and by 26.5% for GK stars; systematic bias for log g in metal-poor ([Fe/H] < –1.0) stars is significantly mitigated (Zhang et al., 18 Sep 2025).
• Maximum-Likelihood Metallicity Estimation: Color-color loci defined by the SAGES uv filters, combined with Gaia (G_BP–G_RP)_0 and maximum-likelihood modeling, yield metallicities to [Fe/H] ≃ –3.5 with typical uncertainties of 0.1–0.15 dex under [Fe/H] > –1 and 0.15–0.4 dex at lower metallicity. This is sufficient for robust identification of VMP/EMP stars (Huang et al., 2023, Hong et al., 2023).

Infrared (2MASS, WISE) and ultraviolet (GALEX) photometric data, when incorporated, provide additional leverage on stellar parameters due to reduced extinction sensitivity (Gu et al., 5 Feb 2025).

4. Key Scientific Results and Applications

SAGES’s high-fidelity catalog—comprising nearly 50 million stars in DR1 with Gaia-matched astrometry—enables:

• Galactic Metallicity Distribution: Construction of MDFs and mapping of metal-poor star distribution across the northern sky, with over five million stars found at [Fe/H] ≤ –1.0 and about one million at [Fe/H] ≤ –2.0 (Huang et al., 2023).
• Identification of Disk VMP/EMP Stars: Using full 6D phase-space information and color-calibrated metallicities, several hundred candidate metal-poor disk stars ([Fe/H] ≤ –2.0) with high-prograde kinematics and low eccentricity were isolated, suggesting the existence of a primordial disk component at the lowest metallicities (Hong et al., 2023).
• Stellar Parameter Uniformity: Benchmarks against clusters and well-studied samples show agreement in [Fe/H] dispersion at the 0.1–0.15 dex level, effective for galactic archaeology and for candidate selection in spectroscopic follow-up.
• Surface Gravity and Classification: The DDO51 filter, by isolating Mg b absorption, enables accurate dwarf/giant separation even at low metallicities. Classification performance for GK stars is particularly improved post-DDO51, critical for population studies and for controlling distance and absolute magnitude biases (Zhang et al., 18 Sep 2025).
• Ancillary Science: The survey’s uniform depth and spatial coverage are designed for detection of white dwarf candidates, stellar flares, and variable objects. The α n–α w narrow-band pair is optimized for high-fidelity extinction correction, enabling construction of spatially resolved 3D extinction maps.

5. Data Releases, Catalog Infrastructure, and Precision Benchmarks

• DR1 Master Catalog: Contains 48,553,987 sources with at least uₛ, vₛ, and all PS1 grizy measurements (Fan et al., 2023).
• g/r/i Data Release: NOWT-based imaging covers ~4600 deg² with over 109 million source detections, yielding ~51 million unique sources, and provides a supplement for SDSS at the bright end (Li et al., 14 Oct 2024).
• Calibration and Uniformity: Relative photometric uniformity of 1–2 mmag per band is demonstrated; astrometric precision is <0.2″—internally referenced to Gaia and externally validated with PS1 and synthetic Gaia BP/RP photometry (Xiao et al., 2023).
• Catalog Structure: Each source entry includes SAGES_ID, astrometry, bandpass photometry/error, quality flags, and, in future releases, machine-learned atmospheric parameters. Field overlap allows multiple-visit averaging to further suppress systematics.

6. Scientific Impact and Future Directions

SAGES establishes a new standard for wide-area, intermediate/narrow-band photometric surveys in Galactic astronomy:

• Galactic Archaeology: The unprecedented combination of photometric accuracy, coverage, and spectral sensitivity enables discrimination of chemical and dynamical substructures, supports mapping of ancient disk, halo, and accretion events, and assists with identification of rare evolutionary phases and nucleosynthetic signatures.
• Synergy with Spectroscopic Surveys: SAGES’s catalog is directly applicable for efficient targeting in multi-object spectroscopy (e.g., LAMOST, WEAVE), facilitating studies of VMP/EMP abundance patterns, kinematics, and age distributions on a galactic scale.
• Expansion with Additional Filters: Anticipated future releases with DDO51, Hα, and further medium/narrow bands will enhance log g, Tₑff, and [Fe/H] determinations, reducing systematic degeneracies and enabling direct inference of other chemical elemental abundances with photometric proxies (Zhang et al., 18 Sep 2025, Gu et al., 5 Feb 2025).
• Foundation for Next-Generation Surveys: The SAGES methodology and calibration protocols provide a template for future global photometric surveys seeking to combine filter design, machine learning, and astrometric reference to deliver spectroscopic-quality parameterization at the survey scale.

7. Relevant Formulas and Calibration Models

SAGES data processing and parameter inference utilize numerous equations and polynomial models, a selection of which is summarized below:

• Astrometric SIP Distortion Model:

$$[\epsilon \; \eta]^T = CD \times \begin{bmatrix} u + f(u,v) \ v + g(u,v) \end{bmatrix}^T$$

where $f(u,v)$ and $g(u,v)$ are 2D polynomials up to order 3 (Fan et al., 2023).

• SCR/Photometric Calibration:

$$C_0 = a_0 + a_1 T_\mathrm{eff} + a_2 \mathrm{[Fe/H]} + a_3 T_\mathrm{eff}^2 + a_4 T_\mathrm{eff}\mathrm{[Fe/H]} + a_5 \mathrm{[Fe/H]}^2$$

with zero-points further modeled as functions of (X,Y) CCD coordinates (Xiao et al., 2023).

• Machine-Learning Metallicity Locus:

$$(u/v-G_{BP})_0 = \sum_{i+j \leq 3} a_{ij} x^i y^j,\;\; x = (G_{BP}-G_{RP})_0,\; y = [\mathrm{Fe/H}]$$

with maximum-likelihood estimation for individual [Fe/H] determinations (Huang et al., 2023).

• DDO51-based Surface Gravity Diagnostics:

$\Delta \mathrm{Mg\; I\,b} \propto \mathrm{log}\,g$

with improvement in log g precision by 21–26.5% relative error (Zhang et al., 18 Sep 2025).

All SAGES catalog and image data are available via China-VO and NADC (https://nadc.china-vo.org), with data schema, calibration relations, and machine-learning model details referenced in the corresponding data release publications (Fan et al., 2023, Li et al., 14 Oct 2024).

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This comprehensive infrastructure positions SAGES as a key instrument for Galactic studies, delivering the scale, accuracy, and parameter fidelity necessary for systematic mapping of chemical and dynamical evolution in the Milky Way and its immediate extragalactic environment.