AGNfitter : a Bayesian MCMC approach to fitting spectral energy distributions of AGNs.

摘要

We present AGNfitter, a publicly available open-source algorithm implementing a fully Bayesian Markov ChainudMonte Carlo method to fit the spectral energy distributions (SEDs) of active galactic nuclei (AGNs) from the submillimeterudto the UV, allowing one to robustly disentangle the physical processes responsible for their emission.udAGNfitter makes use of a large library of theoretical, empirical, and semi-empirical models to characterize both theudnuclear and host galaxy emission simultaneously. The model consists of four physical emission components: anudaccretion disk, a torus of AGN heated dust, stellar populations, and cold dust in star-forming regions. AGNfitteruddetermines the posterior distributions of numerous parameters that govern the physics of AGNs with a fullyudBayesian treatment of errors and parameter degeneracies, allowing one to infer integrated luminosities, dustudattenuation parameters, stellar masses, and star-formation rates. We tested AGNfitter’s performance on real data byudfitting the SEDs of a sample of 714 X-ray selected AGNs from the XMM-COSMOS survey, spectroscopicallyudclassified as Type1 (unobscured) and Type2 (obscured) AGNs by their optical–UV emission lines. We find thatudtwo independent model parameters, namely the reddening of the accretion disk and the column density of the dustyudtorus, are good proxies for AGN obscuration, allowing us to develop a strategy for classifying AGNs as Type1 orudType2, based solely on an SED-fitting analysis. Our classification scheme is in excellent agreement with theudspectroscopic classification, giving a completeness fraction of ~86% and ~70%, and an efficiency of ~80% andud~77%, for Type1 and Type2 AGNs, respectively.