Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3

Abstract

By combining spectroscopic data from the LAMOST DR7, Sloan Digital Sky Survey (SDSS) DR12, and corrected photometric data from the Gaia EDR3, we apply the stellar color regression (SCR) method to recalibrate the SDSS Stripe 82 standard stars catalog of Ivezić et al. With a total number of about 30,000 spectroscopically targeted stars, we have mapped out the relatively large and strongly correlated photometric zero-point errors present in the catalog, ∼2.5% in the u band and ∼1% in the griz bands. Our study also confirms some small but significant magnitude dependence errors in the z band for some charge-coupled devices. Various tests show that we have achieved an internal precision of about 5 mmag in the u band and about 2 mmag in the griz bands, which is about five times better than previous results. We also apply the method to the latest version of the catalog (v4.2), and find modest systematic calibration errors of up to ∼1% along the R.A. direction and smaller errors along the decl. direction. The results demonstrate the power of the SCR method when combining spectroscopic data and Gaia photometry in breaking the 1% precision barrier of ground-based photometric surveys. Our work paves the way for the recalibration of the whole SDSS photometric survey and has important implications for the calibration of future surveys. Future implementations and improvements of the SCR method under different situations are also discussed.

Empirical Temperature- and Extinction-dependent Extinction Coefficients for the GALEX, Pan-STARRS 1, Gaia, SDSS, 2MASS, and WISE Passbands

“…With the rapid development of data-processing and calibration techniques (e.g., Padmanabhan et al 2008;Yuan et al 2015;Burke et al 2018;Huang & Yuan 2022), many photometric surveys are able to deliver magnitudes and colors to a precision of approximately 1% for numerous targets. Upcoming surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST; Ivezić et al 2019) and the Chinese Space Station Telescope (CSST; Zhan 2018), are going to provide even better photometric data.…”

“…On the other hand, the intrinsic colors of stars can be predicted with a precision of approximately 1% from spectroscopic surveys, making it possible to achieve millimagnitude-precision calibration of photometric surveys by the stellar color regression method (SCR; Yuan et al 2015;Bowen et al 2022). To compare predicted colors with observed colors and perform high-precision calibration with the SCR method, precise reddening correction is also required (e.g., Niu et al 2021aNiu et al , 2021bXiao & Yuan 2022;Huang & Yuan 2022). Reddening correction is becoming the critical factor that limits the optimal use of high-quality photometric, astrometric, and spectroscopic data.…”

GaiaData Release 3

“…The acquisition of multi-colour photometry for many millions of stars over huge areas of the sky, with strictly the same setup and innovative techniques of photometric calibration, has allowed for the first time to achieve precision of < 0.01 mag on an industrial scale. This achievement converted the de facto closed systems associated to these surveys into open systems, providing abundant standard stars with which to transform suitable observations taken outside the survey into the standard system that they define (see Huang & Yuan 2022, for a synthetic review and references on modern surveys and calibration techniques).…”

“…Indeed, this exquisite degree of internal homogeneity has been used to significantly reduce residual systematic errors in the best set of SDSS standard stars (see e.g. Thanjavur et al 2021;Huang & Yuan 2022).…”

Empirical temperature- and extinction-dependent extinction coefficients for the GALEX, Pan-STARRS1, Gaia, SDSS, 2MASS, and WISE passbands

“…On the other hand, the intrinsic colors of stars can be predicted with a precision of approximately 1% from spectroscopic surveys, making it possible to achieve mmag precision calibration of photometric surveys by the stellar color regression method (SCR; Yuan et al 2015;Bowen et al 2022). To compare predicted colors with observed colors and perform high-precision calibration with the stellar color regression (SCR) method, precision reddening correction is also required (e.g., Niu et al 2021a,b;Huang & Yuan 2022;Xiao & Yuan 2022). Reddening correction is becoming the critical factor that limits the optimal use of high-quality photometric, astrometric, and spectroscopic data.…”