Geometric and Dynamical Models of Reverberation Mapping Data

TL;DR

This paper introduces a comprehensive Bayesian method to analyze reverberation mapping data, enabling simultaneous estimation of black hole mass and BLR geometry and dynamics with realistic uncertainties.

Contribution

The authors develop a novel, self-consistent approach that infers the spatial and velocity distribution of the BLR directly from data, removing the need for a virial coefficient.

Findings

Successfully recover BLR parameters with ~0.1 dex uncertainty in simulated data.

Achieve ~0.05 dex accuracy in black hole mass and radius with high-quality data.

Method provides realistic uncertainties depending on data quality and AGN variability.

Abstract

We present a general method to analyze reverberation mapping data that provides both estimates for the black hole mass and for the geometry and dynamics of the broad line region (BLR) in active galactic nuclei (AGN). Our method directly infers the spatial and velocity distribution of the BLR from the data, allowing us to easily derive a velocity-resolved transfer function and allowing for a self-consistent estimate of the black hole mass without a virial coefficient. We obtain estimates and reasonable uncertainties of the BLR model parameters by implementing a Markov Chain Monte Carlo algorithm using the formalism of Bayesian probability theory. We use Gaussian Processes to interpolate the the continuum light curve data and create mock light curves that can be fitted to the data. We test our method by creating simulated reverberation mapping data-sets with known true parameter values and by trying to recover these parameter values using our models. We are able to recover the parameters with realistic uncertainties that depend upon the variability of the AGN and the quality of the reverberation mapping campaign. With a geometry model we can recover the mean radius of the BLR to within ~0.1dex random uncertainty for simulated data with an integrated line flux uncertainty of 1.5%, while with a dynamical model we can recover the black hole mass and the mean radius to within ~0.05dex random uncertainty, for simulated data with a line profile average signal to noise ratio of 4 per spectral pixel. These uncertainties do not include modeling errors, which are likely to be present in the analysis of real data, and should therefore be considered as lower limits to the accuracy of the method.