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

Abstract

Abstract As one of the best ground-based photometric data set, Pan-STARRS1 (PS1) has been widely used as the reference to calibrate other surveys. In this work, we present an independent validation and recalibration of the PS1 photometry using spectroscopic data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) DR7, and photometric data from the corrected Gaia Early Data Release 3 (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 ∼ 5 mmag when averaged over 20′ regions. However, significant large- and small-scale spatial variation of magnitude offset, up to over 1%, probably caused by the calibration errors in the PS1, are found for all the grizy filters. The calibration errors in different filters are uncorrelated, 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 mag 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.