Beyond Spectroscopy. II. Stellar Parameters for over 20 Million Stars in the Northern Sky from SAGES DR1 and Gaia DR3

Abstract

We present precise photometric estimates of stellar parameters, including effective temperature, metallicity, luminosity classification, distance, and stellar age, for nearly 26 million stars using the methodology developed in the first paper of this series, based on the stellar colors from the Stellar Abundances and Galactic Evolution Survey (SAGES) Data Release 1 and Gaia Early Data Release 3. The optimal design of stellar-parameter sensitive uv filters by SAGES has enabled us to determine photometric-metallicity estimates down to −3.5, similar to our previous results with the SkyMapper Southern Survey (SMSS), yielding a large sample of over five million metal-poor ([Fe/H] ≤ −1.0) stars and nearly one million very metal-poor ([Fe/H] ≤ −2.0) stars. The typical precision is around 0.1 dex for both dwarf and giant stars with [Fe/H] > −1.0, and 0.15–0.25/0.3–0.4 dex for dwarf/giant stars with [Fe/H] < −1.0. Using the precise parallax measurements and stellar colors from Gaia, effective temperature, luminosity classification, distance, and stellar age are further derived for our sample stars. This huge data set in the Northern sky from SAGES, together with similar data in the Southern sky from SMSS, will greatly advance our understanding of the Milky Way, in particular its formation and evolution.

Stellar Loci. VII. Photometric Metallicities of 5 Million FGK Stars Based on GALEX GR6+7 AIS and Gaia EDR3

“…Photometric metallicity measurements are often overestimated for VMP/EMP stars, due to contamination of the blue narrow/medium-band filters by molecular carbon bands such as CN (e.g., Hong et al 2023;Huang et al 2023). In this subsection, we check whether the strong molecular carbon bands strongly influence the photometric metallicity estimates found by the use of the NUV band.…”

“…Notably, they observed systematic differences between the metallicitydependent stellar loci of red giants and main-sequence stars. Huang et al (2019Huang et al ( , 2022Huang et al ( , 2023 Broadband photometry is generally believed to be unsuitable for photometric metallicity estimation, due to its very weak sensitivity to changes in stellar metallicity. However, by combining data from Gaia Early Data Release 3 (EDR3; Brown et al 2021) and LAMOST DR7 (Luo et al 2015), and using the stellar loci fitting technique, Xu et al (2022a, Paper V) obtained metallicity estimates for about 27 million stars (including over 20 million dwarfs and 6 million giants) with 10 < G < 16 across almost the entire sky.…”

On the Performances of Estimating Stellar Atmospheric Parameters from CSST Broad-band Photometry

“…As shown in Figure 8, the sequences of different metallicities ranging from [Fe/H] = −4 to [Fe/H] = +0.5, as color (u − g) changing with (g − i), can be seen clearly for both dwarf and giant stars. Similar to previous studies (Yuan et al 2015;Huang et al 2022;Lin et al 2022;Huang et al 2023), third-order 2D polynomials are adopted to fit the color (u − g), as a function of (g − i) and [Fe/H] for dwarf and giant stars, respectively:…”

“…On the other hand, as summarized in Huang et al (2022), stellar atmospheric parameters in the particular metallicity of dozens of million stars can be precisely measured from photometric surveys with near-ultraviolet bands such as the Sloan Digital Sky Survey (SDSS, Yorket al 2000), the SkyMapper Southern Survey (SMSS, Wolf et al 2018;Onken et al 2019), the Pristine survey (Starkenburg et al 2017) ). The recent efforts based on surveys with narrow/medium near-ultraviolet bands have shown the precision of metallicity is comparable to that from low/medium resolution spectroscopy (e.g., Huang et al 2019Huang et al , 2022Lin et al 2022;Huang et al 2023).…”

J-PLUS: Photometric Recalibration with the Stellar Color Regression Method and an Improved Gaia XP Synthetic Photometry Method

“…Most recently, B. Huang et al (2023, in preparation) conducted a comprehensive correction of the magnitude-, color-, and extinction-dependent systematic errors in the Gaia XP spectra, drawing upon data from CALSPEC (Bohlin et al 2014;Bohlin & Lockwood 2022) and Hubbleʼs Next Generation Spectral Library (Koleva & Vazdekis 2012). This correction process also incorporated the spectroscopy-based SCR method (Yuan et al 2015a).…”