PyTICS: An Iterative Method for Photometric Lightcurve Intercalibration using Comparison Stars

TL;DR

This paper introduces an iterative intercalibration algorithm for photometric lightcurves using comparison stars, improving accuracy and uncertainty quantification in AGN monitoring data from multiple telescopes.

Contribution

The novel algorithm simultaneously calibrates telescope and epoch corrections while modeling noise, without assumptions on AGN variability, applicable to any astronomical object.

Findings

More accurate uncertainty quantification compared to existing tools.

Effective identification of problematic epochs, telescopes, and stars.

Applicable to diverse astronomical objects without variability assumptions.

Abstract

Intensive reverberation mapping monitoring programs combine ground-based photometric observations from different telescopes, requiring intercalibration of lightcurves to reduce systematic instrumental differences. We present a new iterative algorithm to calibrate photometric time-series data of active galactic nuclei (AGN) 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 multi-component 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 lightcurves 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.