Photometric recalibration of the SDSS Stripe 82 to a few milimagnitude precision with the stellar color regression method and Gaia EDR3

TL;DR

This paper uses Gaia EDR3 and spectroscopic data to recalibrate SDSS Stripe 82 with millimagnitude precision, revealing and correcting significant zero-point errors and systematic biases in the photometric catalog.

Contribution

It introduces an improved Stellar Color Regression method combining Gaia and spectroscopic data to achieve sub-millimagnitude calibration accuracy for large photometric surveys.

Findings

Achieved 5 mmag precision in u band and 2 mmag in griz bands, surpassing previous results.

Mapped and corrected large zero-point errors (~2.5% in u, ~1% in griz) in SDSS Stripe 82.

Identified and addressed magnitude dependence errors in the z band for some CCDs.

Abstract

By combining spectroscopic data from the LAMOST DR7, SDSS DR12, and corrected photometric data from the Gaia EDR3, we apply the Stellar Color Regression (SCR; Yuan et al. 2015a) method to recalibrate the SDSS Stripe 82 standard stars catalog of Ivezi\'c et al. (2007). 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, $\sim$2.5 per cent in the $u$ band and $\sim$ 1 per cent 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 5 times better than previous results. We also apply the method to the latest version of the catalog (V4.2; Thanjavur et al. 2021), and find modest systematic calibration errors up to $\sim$ 1 per cent along the R.A. direction and smaller errors along the Dec. direction. The results demonstrate the power of the SCR method when combining spectroscopic data and Gaia photometry in breaking the 1 percent precision barrier of ground-based photometric surveys. Our work paves the way for the re-calibration 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.