The Radius-Luminosity Relationship for Active Galactic Nuclei with Hierarchical Bayesian Reverberation Mapping
Abstract
Reverberation mapping of active galactic nuclei uses the time taken for variability to travel through the system as a measure of system size, with this size being used to inform estimates of the mass of the central black hole, a technique that has graduated to an industrial-scale effort in recent decades. Such large target-count, multi-year surveys have been consistently undercut by poor light curve quality and the aliasing problems of seasonal observations. These impacts compound with statistical approaches that are not consistent between works and sometimes combine inconsistent statistical frameworks. Here, we present a new end-to-end Bayesian framework for fitting lags at scale in large surveys, simultaneous with the fitting of population models such as the radius-luminosity relation, and formulated in a numerically tractable way. This procedure is a statistically and numerically robust generalisation of the stacking and quality cut procedures of past works, fully marginalising over data uncertainty and data quality. Our approach remains numerically stable when scaling to larger surveys, and can act as a basis for any Bayesian population modelling of large sets of AGN time series observations. Applied to the OzDES sample, we demonstrate that this new approach yields more than double the constraining power on population-level properties like the R-L relationship, and an order-of-magnitude increase in the number of recovered lags and masses. With this method, we go from 24 high quality lag recoveries with traditional methods to a new effective sample size of ~297, all while being robust against random chance false positive measurements.
Related
- A Hierarchical Bayesian Differential Calibration of the Gaia DR3 Parallax Zero Point Using Galactic Globular Clusters
- TeV Gamma Rays and Neutrinos from Winds Driven by Radiatively Inefficient Accretion Flows of Low-Luminosity Active Galactic Nuclei
- Differential Luminosity Response of Balmer-Line Profiles in Galactic B/A Luminous Stars
- PeV Diffuse Neutrinos from Magnetically Arrested Disks in Low-Luminosity Active Galactic Nuclei
- Aligned Hierarchical Black Hole Mergers in Active-Galactic-Nuclei Disks Revealed by GWTC-4
BibTeX
@misc{mcdougall2026radiusluminosity,
title = {{The Radius-Luminosity Relationship for Active Galactic Nuclei with Hierarchical Bayesian Reverberation Mapping}},
author = {Hugh McDougall and Tamara M. Davis and Benjamin J. S. Pope and Paul Martini and Zhefu Yu and Chris Lidman and Geraint F. Lewis and Christian Adamcewicz and Rahma Alfarsy},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.22096},
eprint = {2610.22096},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.22096},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - The Radius-Luminosity Relationship for Active Galactic Nuclei with Hierarchical Bayesian Reverberation Mapping
AU - Hugh McDougall
AU - Tamara M. Davis
AU - Benjamin J. S. Pope
AU - Paul Martini
AU - Zhefu Yu
AU - Chris Lidman
AU - Geraint F. Lewis
AU - Christian Adamcewicz
AU - Rahma Alfarsy
PY - 2026
DA - 2026/10/15
PB - arXiv
DO - 10.48550/arxiv.2610.22096
UR - https://arxiv.org/abs/2610.22096
AB - Reverberation mapping of active galactic nuclei uses the time taken for variability to travel through the system as a measure of system size, with this size being used to inform estimates of the mass of the central black hole, a technique that has graduated to an industrial-scale effort in recent decades. Such large target-count, multi-year surveys have been consistently undercut by poor light curve quality and the aliasing problems of seasonal observations. These impacts compound with statistical approaches that are not consistent between works and sometimes combine inconsistent statistical frameworks. Here, we present a new end-to-end Bayesian framework for fitting lags at scale in large surveys, simultaneous with the fitting of population models such as the radius-luminosity relation, and formulated in a numerically tractable way. This procedure is a statistically and numerically robust generalisation of the stacking and quality cut procedures of past works, fully marginalising over data uncertainty and data quality. Our approach remains numerically stable when scaling to larger surveys, and can act as a basis for any Bayesian population modelling of large sets of AGN time series observations. Applied to the OzDES sample, we demonstrate that this new approach yields more than double the constraining power on population-level properties like the R-L relationship, and an order-of-magnitude increase in the number of recovered lags and masses. With this method, we go from 24 high quality lag recoveries with traditional methods to a new effective sample size of ~297, all while being robust against random chance false positive measurements.
ER -