TL;DR
This paper introduces a Bayesian method using the running optimal average (ROA) to analyze quasar lightcurves, accurately measuring time delays and detecting microlensing effects, with applications in gravitational lensing studies.
Contribution
The paper presents a novel Bayesian ROA-based approach for modeling quasar lightcurves, enabling precise time delay measurements and robust outlier handling, validated with simulations and real data.
Findings
ROA method accurately measures quasar time delays
Comparison shows ROA results align with existing methods
Detected microlensing effects in COSMOGRAIL data
Abstract
We present a new method of modelling time-series data based on the running optimal average (ROA). By identifying the effective number of parameters for the ROA model, in terms of the shape and width of its window function and the times and accuracies of the data, we enable a Bayesian analysis, optimising the ROA width, along with other model parameters, by minimising the Bayesian Information Criterion (BIC) and sampling joint posterior parameter distributions using MCMC methods. For analysis of quasar lightcurves, our implementation of ROA modelling can measure time delays among lightcurves at different wavelengths or from different images of a lensed quasar and, in future work, be used to inter-calibrate lightcurve data from different telescopes and estimate the shape and thus the power-density spectrum of the lightcurve. Our noise model implements a robust treatment of outliers and…
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