Modelling the Multiwavelength Variability of Mrk 335 using Gaussian Processes

Abstract

The optical and UV variability of the majority of AGN may be related to the reprocessing of rapidlychanging X-ray emission from a more compact region near the central black hole. Such a reprocessing model would be characterised by lags between X-ray and optical/UV emission due to differences in light travel time. Observationally however, such lag features have been difficult to detect due to gaps in the lightcurves introduced through factors such as source visibility or limited telescope time. In this work, Gaussian process regression is employed to interpolate the gaps in the Swift X-ray and UV lightcurves of the narrow-line Seyfert 1 galaxy Mrk 335. In a simulation study of five commonly-employed analytic Gaussian process kernels, we conclude that the Matern 1 2 and rational quadratic kernels yield the most well-specified models for the X-ray and UVW2 bands of Mrk 335. In analysing the structure functions of the Gaussian process lightcurves, we obtain a broken power law with a break point at 125 days in the UVW2 band. In the X-ray band, the structure function of the Gaussian process lightcurve is consistent with a power law in the case of the rational quadratic kernel whilst a broken power law with a break point at 66 days is obtained from the Matern 1 2 kernel. The subsequent cross-correlation analysis is consistent with previous studies and furthermore, shows tentative evidence for a broad X-ray-UV lag feature of up to 30 days in the lag-frequency spectrum where the significance of the lag depends on the choice of Gaussian process kernel.

Detecting the periodicity of highly irregularly sampled light-curves with Gaussian processes: the case of SDSSJ025214.67-002813.7

“…interpolating) our datasets (e.g. Covino et al 2020), yet Bayes factors show very different results for the adopted kernels (Table 2) reflecting their different ability in describing the data covariance (Wilkins 2019;Griffiths et al 2021).…”

“…Then, the problem of determining the presence of a periodic component hidden in the (often correlated) noise affecting an astronomical time series can be addressed by means of Bayesian model selection (Kass & Raftery 1995;Jenkins 2014;Andreon & Weaver 2015;Trotta 2017). Wilkins (2019) and Griffiths et al (2021) carried out extensive simulations verifying the capability of some of the most common kernel functions (e.g. the squared exponential or radial basis, Matérn and rational quadratic kernels, Rasmussen & Williams 2006) to accurately modeling the data PSD.…”