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arXiv · 2202.07168

Validation and Improvement of the Pan-STARRS Photometric Calibration with the Stellar Color Regression Method

Kai Xiao·Haibo Yuan
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Abstract

As one of the best ground-based photometric dataset, Pan-STARRS1 (PS1) has been widely used as the reference to calibrate other surveys. In this work, we present an independent validation and re-calibration of the PS1 photometry using spectroscopic data from the LAMOST DR7 and photometric data from the corrected Gaia EDR3 with the Stellar Color Regression (SCR) method. Using per band typically a total of 1.5 million LAMOST-PS1-Gaia stars as standards, we show that the PS1 photometric calibration precisions in the $grizy$ filters are around $4\sim 5$ mmag when averaged over $20'$ regions. However, significant large- and small-scale spatial variation of magnitude offset, up to over 1 per cent, probably caused by the calibration errors in the PS1, are found for all the $grizy$ filters. The calibration errors in different filters are un-correlated, and are slightly larger for the $g$ and $y$ filters. We also detect moderate magnitude-dependent errors (0.005, 0.005, 0.005, 0.004, 0.003 mag per magnitude in the 14 -- 17 magnitude range for the $grizy$ filters, respectively) in the PS1 photometry by comparing with the Gaia EDR3 and other catalogs. The errors are likely caused by the systematic uncertainties in the PSF magnitudes. We provide two-dimensional maps to correct for such magnitude offsets in the LAMOST footprint at different spatial resolutions from $20'$ to $160'$. The results demonstrate the power of the SCR method in improving the calibration precision of wide-field surveys when combined with the LAMOST spectroscopy and Gaia photometry.

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Kai Xiao, Haibo Yuan. 2022-02-15. Validation and Improvement of the Pan-STARRS Photometric Calibration with the Stellar Color Regression Method. https://doi.org/10.3847/1538-3881%2Fac540a

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