Abstract: AGNfitter is a fully Bayesian MCMC method to fit the spectral energy distributions (SEDs) of active galactic nuclei (AGN) and galaxies from the sub-mm to the UV; it enables robust disentanglement of the physical processes responsible for the emission of sources. Written in Python, AGNfitter makes use of a large library of theoretical, empirical, and semi-empirical models to characterize both the nuclear and host galaxy emission simultaneously. The model consists of four physical emission components: an accretion disk, a torus of AGN heated dust, stellar populations, and cold dust in star forming regions. AGNfitter determines the posterior distributions of numerous parameters that govern the physics of AGN with a fully Bayesian treatment of errors and parameter degeneracies, allowing one to infer integrated luminosities, dust attenuation parameters, stellar masses, and star formation rates.
Credit: Calistro Rivera, Gabriela; Lusso, Elisabeta; Hennawi, Joseph F.; Hogg, David W.
Preferred citation method: http://adsabs.harvard.edu/abs/2016arXiv160605648C