Validation and Improvement of the Pan-STARRS Photometric Calibration with the Stellar Color Regression Method
Kai Xiao, Haibo Yuan

TL;DR
This study validates and refines the Pan-STARRS1 photometric calibration using stellar color regression with LAMOST and Gaia data, revealing spatial and magnitude-dependent errors and providing correction maps.
Contribution
It introduces an independent validation and re-calibration of PS1 photometry using SCR with spectroscopic and Gaia data, improving calibration accuracy.
Findings
PS1 calibration precision is around 4-5 mmag in grizy bands.
Significant spatial variation of calibration errors up to over 1%.
Detection of magnitude-dependent systematic errors in PS1 photometry.
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 filters are around mmag when averaged over 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 filters. The calibration errors in different filters are un-correlated, and are slightly larger for the and…
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