AI Insight
This study investigates the reliability of the broad-line region radius-luminosity relation used to estimate black hole masses in active galactic nuclei. By analyzing approximately 1,200 reverberation mapping measurements and simulating light curves matching actual observational conditions, the researchers found that roughly 40% of measurements show discrepancies due to poor data quality or flawed lag recovery methods. After filtering out problematic data, the intrinsic scatter in the relation decreased from 0.26 dex to 0.11 dex, indicating that most observed scatter stems from observational limitations rather than true physical variation among AGN.
Why it matters
This work provides critical quality control for black hole mass estimates across thousands of active galaxies, which are fundamental to understanding galaxy evolution and supermassive black hole growth. The publicly available database and simulation tools will help astronomers design better observing campaigns and identify reliable measurements for future studies.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Abstract: The broad-line region (BLR) radius-luminosity (R-L) relation underpins single-epoch black hole mass estimates in active galactic nuclei (AGN). The published Hbeta sample is heterogeneous in observational quality, and it remains unclear how much of its scatter is intrinsic rather than caused by systematics in lag recovery. We quantify the contribution of unreliable lag recovery to the observed scatter of the Hbeta R-L relation and provide tools for quality control. We compile a publicly available database of reverberation mapping measurements for ~1200 AGN from 32 campaigns spanning more than three decades, including lags, luminosities, line widths, black hole masses, and observational metadata. We develop a simulation-based consistency framework in which damped random walk light curves are sampled according to each campaign’s baseline, cadence, and signal-to-noise ratio, and lags are recovered with the interpolated cross-correlation function (ICCF). Comparing expected, reported, and simulation-retrieved lags defines a four-tier flagging scheme. We refit the R-L relation with UltraNest for progressively cleaner samples and construct consensus samples across three reference slopes to reduce model dependence. Of 248 Hbeta sources, ~40% show discrepancies between reported and simulation-retrieved lags, while ~5% show direct inconsistencies between expected and retrieved lags. Excluding flagged sources and correcting for model bias reduces the inferred intrinsic scatter from sigma = 0.26^{+0.02}_{-0.01} dex to 0.11pm0.01 dex, with a corrected slope of alpha = 0.48pm0.02. Our results indicate that a substantial fraction of the observed Hbeta R-L scatter arises from observational limitations and lag-recovery biases rather than intrinsic AGN diversity. The database, simulation code, and RM-Scout campaign-planning tool are publicly available.
Source: A simulation-based quality-control framework for the broad-line region radius-luminosity relation