Measuring time delays: I. Using a flux time series that is a linear combination of time-shifted light curves

TL;DR

This paper introduces a likelihood-based method to measure time delays in unresolved, red-noise light curves, aiding in identifying lensed quasars and reverberation timescales, even with low flux ratios.

Contribution

It develops a novel likelihood function for combined, unresolved light curves to detect time delays and flux ratios, improving analysis of complex astrophysical phenomena.

Findings

Effective in measuring time delays with flux ratios as low as 1/10.

Can distinguish between combined and original light curves using the likelihood approach.

Provides practical Python and MATLAB tools for implementation.

Abstract

(Abridged) Several phenomena in astrophysics generate light curves with time delays. Among these are reverberation mapping, and lensed quasars. In some systems, the measurement of the time-delay is complicated by the fact that the delayed components are unresolved and that the light curves are generated from a red-noise process. We derive the likelihood function of the observations given a model of either a combination of time-delayed light curves or a single light curve. This likelihood function is different from the auto-correlation function. We demonstrate that given a single-band light curve that is a combination of two (or more) time-shifted copies of an original light curve, generated from a red-noise probability distribution, we can test if the total-flux light curve is a composition of time-delayed copies or, alternatively, is consistent with being the original light curve. Furthermorew, in some realistic cases, it is possible to measure the time delays and flux ratios between these unresolved components even when the flux ratio is about 1/10. This method is useful for identifying lensed quasars and simultaneously measuring their time delays, and for estimating the reverberation time scales of active galactic nuclei. In a companion paper, we derive a method that uses the center-of-light position (e.g., of a lensed quasar) along with the combined flux. This allow us to identify lensed quasars and supernovae and measure their time delays, with higher fidelity compared to the flux-only method. The astrometry + flux method, however, is not suitable for quasar reverberation mapping. We also comment on the commonly used method of fitting a power-law model to a power spectrum, and present the proper likelihood function for such a fit. We test the new method on simulations and provide Python and MATLAB implementations.