Measuing the Black Hole Mass vs Stellar mass relation via the ensemble variability of AGN
The flux variability observed in the light curves of Active Galactic Nuclei depends on the physical properties of the system, such as the mass of the central supermassive black hole and the accretion rate onto it. This opens the potential to measure black hole masses for individual AGN by analysing their light curves. Recently this arguement has been extended to populations. Extragalactic survey field detect large numbers of AGN at cosmological redshifts. Furthemore, many of these surveys also provide sparse light curves for individual sources, as a result of observations spread over time. These light curves on their own right contain limited and noisy information on the properties of individual AGN. Nevertheless, when studied as an ensemble, i.e. averaged over the entire population, they can provide useful signal on the black hole mass and accretion rate distribution of the sample.
Combining measurements of the ensemble variability of AGN with approprtiate modeling allows constraints on the black hole mass distribution of AGN samples out to high redshift. The potential of this approach is demonstrated on the top panel on the left. It shows measurements of the ensemble variability of AGN in the Chandra Deep Field South survey (Paolillo et al. 2017) as a function of accretion luminosity. The shaded region correspond is the fit to the observations of an empirical model that links the demographics of AGN to their ensemble X-ray variability properties. One of the inputs of the empirical model is a pasteurisation of the Black Hole Mass vs Stellar Mass scaling relation. By fitting the data points of the top panel provides a handle on this relation. This is demonstrated in the bottom panel, which shows the best-fit Black Hole Mass vs Stellar Mass relation based on the ensemble variability measurements in comparison with independent estimates based on local galaxy samples. A full description of the methodology is available in ore details in Georgakakis et al. (2021)