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Geometric and Dynamical Models of Reverberation Mapping Data
Cosmology and Nongalactic Astrophysics · Submitted 25 Jan 2011
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | 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). | contribution 1.00 | 0.93 | 0.43 | asserted 1.00 |
| s1 | 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. | contribution 0.50 | 0.93 | 0.22 | asserted 1.00 |
| s2 | 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. | method 0.99 | 0.95 | 0.05 | asserted 1.00 |
| s3 | We use Gaussian Processes to interpolate the the continuum light curve data and create mock light curves that can be fitted to the data. | method 1.00 | 0.93 | 0.02 | asserted 1.00 |
| s4 | 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. | method 0.95 | 0.96 | 0.03 | asserted 0.98 |
| s5 | 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. | result 0.98 | 0.95 | 0.16 | asserted 0.98 |
| s6 | 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. | result 1.00 | 0.94 | 0.33 | asserted 0.95 |
| s7 | 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. | limitation 1.00 | 0.44 | 0.07 | hedged possibility 0.85 |
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The paper
Original 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.
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References
How the literature uses it
5 arXiv papers cite this in 8 sentences
Papers citing it · 6 sentences without a confident label
Uses its method or tool 1 paper
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BLR dynamical modeling directly infers the virial coefficient for individual AGNs by forward modeling velocity-resolved RM data sets, following the framework first introduced by [ref] and implemented in the Code for AGN Reverberation and Modeling of Emission Lines ().
Can BLR line profile shape improve single-epoch black hole mass estimates? · Introduction · 0.95
What it cites · 4 sentences without a confident label
Uses the method or tool of 1 paper
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The second is Diffusive Nested Sampling [ref] , which provides samples from the posterior PDF and an estimate of the evidence value for the model.
Diffusive Nested Sampling · Method · 1.00
Compares with 1 paper
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We note that our Gaussian Process method for interpolation, in the special case $α=1$, is equivalent to the method of [ref] , apart from computational details. $α=1$ has also been used in detailed studies of quasar variability [ref] .
An Alternative Approach To Measuring Reverberation Lags in Active Galactic Nuclei · Continuum Interpolation · 0.89
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