arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1101.4952astro-ph.COastro-ph.GA
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混响映射数据的几何和动力学模型

Geometric and Dynamical Models of Reverberation Mapping Data

Anna Pancoast, Brendon J. Brewer, Tommaso Treu

摘要:

我们提出了一个通用的方法来分析混响映射数据,提供了估计的黑洞质量和宽线区(BLR)在活动星系核(AGN)的几何形状和动力学。我们的方法直接推断的空间和速度分布的BLR的数据,使我们能够很容易地得到一个速度分辨的传递函数,并允许自洽的估计没有维里系数的黑洞质量。我们得到的估计和合理的不确定性的BLR模型参数通过实施马尔可夫链蒙特卡罗算法使用贝叶斯概率理论的形式主义。我们使用高斯过程来插值连续光变曲线数据,并创建可以拟合到数据的模拟光变曲线。我们测试我们的方法,通过创建模拟混响映射数据集与已知的真实参数值,并试图恢复这些参数值使用我们的模型。我们能够恢复的参数与现实的不确定性,取决于活动星系核的变化和混响映射活动的质量。对于积分线通量不确定度为1.5%的模拟数据,几何模型可以将BLR的平均半径恢复到~0.1dex随机不确定度内,而对于线轮廓平均信噪比为4/光谱像素的模拟数据,动力学模型可以将黑洞质量和平均半径恢复到~0.05dex随机不确定度内。这些不确定性不包括建模误差,建模误差可能存在于实际数据的分析中,因此应被视为该方法准确度的下限。

英文摘要:

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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