PyTICS: an iterative method for photometric light-curve intercalibration using comparison stars

Abstract

ABSTRACT Intensive reverberation mapping monitoring programs combine ground-based photometric observations from different telescopes, requiring intercalibration of light-curves to reduce systematic instrumental differences. We present a new iterative algorithm to calibrate photometric time-series data of active galactic nuclei (AGNs) using 100s of comparison stars on the same images, building upon the established method of ensemble photometry. The algorithm determines telescope-specific and epoch-specific correction parameters, and simultaneously computes a multicomponent noise model to account for underestimated uncertainties based on the scatter in the comparison star data, effectively identifying problematic epochs, telescopes, and stars. No assumptions need to be made about the AGN variability shape, and the algorithm can, in principle, be applied to any astronomical object. We demonstrate our method on light-curves taken with ten 1-m telescopes from the Las Cumbres Observatory (LCO) robotic telescope network. Comparing our results to other intercalibration tools, we find that the algorithm can more accurately quantify the uncertainties in the data. We describe additional corrections that can be made for particularly bluer AGNs like Fairall 9, arising due to systematic effects dependent on star colour.