Using Mutual Information to measure Time-lags from non-linear processes in Astronomy

• Main Authors: Chakraborty, Nachiketa, van Leeuwen, Peter Jan
• Format: Journal Article
• Language: English
• Published: 16.06.2021
• Subjects: Physics - Data Analysis, Statistics and Probability; Physics - High Energy Astrophysical Phenomena; Physics - Instrumentation and Methods for Astrophysics
• DOI: 10.48550/arxiv.2106.08623

Measuring time lags between time-series or lighcurves at different wavelengths from a variable or transient source in astronomy is an essential probe of physical mechanisms causing multiwavelength variability. Time-lags are typically quantified using discrete correlation functions (DCF) which are appropriate for linear relationships. However, in variable sources like X-ray binaries, active galactic nuclei (AGN) and other accreting systems, the radiative processes and the resulting multiwavelength lightcurves often have non-linear relationships. For such systems it is more appropriate to use non-linear information-theoretic measures of causation like mutual information, routinely used in other disciplines. We demonstrate with toy models loopholes of using the standard DCF & show improvements when using the mutual information correlation function (MICF). For non-linear correlations, the latter accurately & sharply identifies the lag components as opposed to the DCF which can be erroneous. Following that we apply the MICF to the multiwavelength lightcurves of AGN NGC 4593. We find that X-ray fluxes lead UVW2 fluxes by ~0.2 days, closer to model predictions from reprocessing by the accretion disk than the DCF estimate. The uncertainties with the current lightcurves are too large though to rule out -ve lags. Additionally, we find another delay component at ~-1 day i.e. UVW2 leading X-rays consistent with inward propagating fluctuations in the accretion disk scenario. This is not detected by the DCF. Keeping in mind the non-linear relation between X-ray & UVW2, this is worthy of further theoretical investigation. From both toy models & real observations, it is clear that the mutual information based estimator is highly sensitive to complex non-linear correlations. With sufficiently high temporal resolution, we will precisely detect each of the lag features corresponding to these correlations.