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arXiv · 1412.1231

Stellar color regression: a spectroscopy based method for color calibration to a few mmag accuracy and the recalibration of Stripe 82

Haibo Yuan·Xiaowei Liu·Maosheng Xiang·Yang Huang·Huihua Zhang·Bingqiu Chen
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Abstract

In this paper, we propose a spectroscopy based Stellar Color Regression (SCR) method to perform accurate color calibration for modern imaging surveys, taking advantage of millions of stellar spectra now available. The method is straightforward, insensitive to systematic errors in the spectroscopically determined stellar atmospheric parameters, applicable to regions that are effectively covered by spectroscopic surveys, and capable of delivering an accuracy of a few millimagnitudes for color calibration. As an illustration, we have applied the method to the SDSS Stripe 82 data (Ivezic et al; I07 hereafter). With a total number of 23,759 spectroscopically targeted stars, we have mapped out the small but strongly correlated color zero point errors present in the photometric catalog of Stripe 82, and improve the color calibration by a factor of 2 -- 3. Our study also reveals some small but significant magnitude dependence errors in z-band for some CCDs. Such errors are likely to be present in all the SDSS photometric data. Our results are compared with those from a completely independent test based on the intrinsic colors of red galaxies presented by I07. The comparison as well as other tests shows that the SCR method has achieved a color calibration internally consistent at a level of about 5 mmag in u-g, 3 mmag in g-r, and 2 mmag in r-i and i-z, respectively. Given the power of the SCR method, we discuss briefly the potential benefits by applying the method to existing, on-going, and up-coming imaging surveys.

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Haibo Yuan, Xiaowei Liu, Maosheng Xiang, Yang Huang, Huihua Zhang, Bingqiu Chen. 2014-12-03. Stellar color regression: a spectroscopy based method for color calibration to a few mmag accuracy and the recalibration of Stripe 82. https://doi.org/10.1088/0004-637x%2F799%2F2%2F133

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