An UBVRI calibration method based on Pan-STARRS photometric survey

Abstract

The Landolt’s standard stars catalog is the most widely utilized for CCD astronomy in the $UBVRI$ broad band photometry system. The Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) is one of the usage of the newly $grizy$ photometric system. In this work, the main objective of this paper is to calibrate observations in the U, B, V, R, and I bands by utilizing data in the g, r, and i bands from the literature. First, the machine learning based method XGBoost is employed to train a model for selecting objects with linear relations between the two catalogs. Then, the color transformation method is utilized to convert objects with $gri$ magnitudes of Pan-STARRS1 (PS1) catalog to the $UBVRI$ system, and a set of transformation coefficients is presented. A photometric calibration system is developed and the color based calibration method is implemented in the system. The typical calibration errors of standard field $GD279$ from instrumental magnitudes to those of standard $UBVRI$ system are derived as 0.173$$mag, 0.053$$mag, 0.024$$mag, 0.019$$mag, 0.020$$mag, respectively. Furthermore, the light curve comparison of SN 2017hpa of a large number of observations from different nights suggests that this method can be qualified for various astrometric observations.