2016/08/31 by Yanrong Li, Yan-Rong Li, Jian-Min Wang +2 · 1 citation
Engineering · Mathematics · Physics and Astronomy · #Active galactic nucleus #Algorithm #Astrophysics #Astrophysics and Cosmic Phenomena #Computer science #Function (biology) #Galaxies: Formation, Evolution, Phenomena #Galaxy #Gaussian #Gaussian process #Mathematics #Parametric statistics #Physics #Quantum mechanics #Reverberation mapping #Statistical physics #Statistics #Structural Health Monitoring Techniques #Transfer function #astro-ph.GA #astro-ph.IM
paper · pdf · doi:10.3847/0004-637x/831/2/206
published as ApJ, 2016, 831, 206 · 12 pages, 10 figures; updated to match the published version
openalex publication_date 2016/11/09 · arxiv created 2016/12/06 · arxiv updated 2016/12/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
ABSTRACT Broad emission lines of active galactic nuclei stem from a spatially extended region (broad-line region, BLR) that is composed of discrete clouds and photoionized by the central ionizing continuum. The temporal behaviors of these emission lines are blurred echoes of continuum variations (i.e., reverberation mapping, RM) and directly reflect the structures and kinematic information of BLRs through the so-called transfer function (also known as the velocity-delay map). Based on the previous works of Rybicki and Press and Zu et al., we develop an extended, non-parametric approach to determine the transfer function for RM data, in which the transfer function is expressed as a sum of a family of relatively displaced Gaussian response functions. Therefore, arbitrary shapes of transfer functions associated with complicated BLR geometry can be seamlessly included, enabling us to relax the presumption of a specified transfer function frequently adopted in previous studies and to let it be determined by observation data. We formulate our approach in a previously well-established framework that incorporates the statistical modeling of continuum variations as a damped random walk process and takes into account long-term secular variations which are irrelevant to RM signals. The application to RM data shows the fidelity of our approach.