NASA/IPAC EXTRAGALACTIC DATABASE
Date and Time of the Query: 2019-06-25 T06:42:19 PDT
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For refcode 2013ApJ...779..110L:
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2013ApJ...779..110L A Bayesian Approach to Estimate the Size and Structure of the Broad-line Region in Active Galactic Nuclei Using Reverberation Mapping Data Li, Yan-Rong; Wang, Jian-Min; Ho, Luis C.; Du, Pu; Bai, Jin-Ming Abstract. This is the first paper in a series devoted to the systematic study of the size and structure of the broad-line region (BLR) in active galactic nuclei (AGNs) using reverberation mapping (RM) data. We employ a recently developed Bayesian approach that statistically describes the variability as a damped random walk process and delineates the BLR structure using a flexible disk geometry that can account for a variety of shapes, including disks, rings, shells, and spheres. We allow for the possibility that the line emission may respond non-linearly to the continuum, and we detrend the light curves when there is clear evidence for secular variation. We use a Markov Chain Monte Carlo implementation based on Bayesian statistics to recover the parameters and uncertainties for the BLR model. The corresponding transfer function is obtained self-consistently. We tentatively constrain the virial factor used to estimate black hole masses; more accurate determinations will have to await velocity-resolved RM data. Application of our method to RM data with Hbeta monitoring for about 40 objects shows that the assumed BLR geometry can reproduce quite well the observed emission-line fluxes from the continuum light curves. We find that the Hbeta BLR sizes obtained from our method are on average ~20% larger than those derived from the traditional cross-correlation method. Nevertheless, we still find a tight BLR size-luminosity relation with a slope of {alpha} = 0.55 +/- 0.03 and an intrinsic scatter of ~0.18 dex. In particular, we demonstrate that our approach yields appropriate BLR sizes for some objects (such as Mrk 142 and PG 2130+099) where traditional methods previously encountered difficulties. Key words: galaxies: active, methods: data analysis, methods: statistical, quasars: general
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