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

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.

Empirical Temperature- and Extinction-dependent Extinction Coefficients for the GALEX, Pan-STARRS 1, Gaia, SDSS, 2MASS, and WISE Passbands

“…On the other hand, the intrinsic colors of stars can be predicted with a precision of approximately 1% from spectroscopic surveys, making it possible to achieve millimagnitude-precision calibration of photometric surveys by the stellar color regression method (SCR; Yuan et al 2015;Bowen et al 2022). To compare predicted colors with observed colors and perform high-precision calibration with the SCR method, precise reddening correction is also required (e.g., Niu et al 2021aNiu et al , 2021bXiao & Yuan 2022;Huang & Yuan 2022). Reddening correction is becoming the critical factor that limits the optimal use of high-quality photometric, astrometric, and spectroscopic data.…”

S-PLUS: Photometric Recalibration with the Stellar Color Regression Method and an Improved Gaia XP Synthetic Photometry Method

“…To correct for reddening, we adopt the same procedure as that in Xiao & Yuan (2022) and Xiao et al (2023c). We adopt the values of E(G BP − G RP ) obtained with the star pair method (Yuan et al 2013;Zhang & Yuan 2020), and the reddening coefficients with respect to E(G BP − G RP ) for the 12 colors constructed by H. Yuan et al (2024, in preparation).…”

“…Huang & Yuan (2022) applied the method to SDSS Stripe 82 standard-star catalogs (Ivezić et al 2007;Thanjavur et al 2021), achieving a precision of 5 mmag in the SDSS u band and of 2 mmag in the griz bands (Yuan et al 2015). In addition, Xiao & Yuan (2022) and Xiao et al (2023b) applied the SCR method to the Pan-STARRS1 (PS1; Tonry et al 2012) data, effectively correcting for significant largescale and small-scale spatial variations in the magnitude offsets and magnitude-dependent systematic errors. Other applications include that of Xiao et al (2023c), who used the SCR method to perform recalibration on the J-PLUS Data Release 3 (DR3) photometric data, accurately measuring and correcting for PS1 systematic errors and metallicity-dependent systematic errors present in the data.…”

Empirical temperature- and extinction-dependent extinction coefficients for the GALEX, Pan-STARRS1, Gaia, SDSS, 2MASS, and WISE passbands

“…On the other hand, the intrinsic colors of stars can be predicted with a precision of approximately 1% from spectroscopic surveys, making it possible to achieve mmag precision calibration of photometric surveys by the stellar color regression method (SCR; Yuan et al 2015;Bowen et al 2022). To compare predicted colors with observed colors and perform high-precision calibration with the stellar color regression (SCR) method, precision reddening correction is also required (e.g., Niu et al 2021a,b;Huang & Yuan 2022;Xiao & Yuan 2022). Reddening correction is becoming the critical factor that limits the optimal use of high-quality photometric, astrometric, and spectroscopic data.…”