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We used the CLUMPY torus models and a Bayesian approach to fit the infrared spectral energy distributions (SEDs) and ground-based high-angular resolution mid-infrared spectroscopy of 13 nearby Seyfert galaxies. This allowed us to put tight constraints on torus model parameters such as the viewing angle, the radial thickness of the torus Y, the angular size of the cloud distribution sigma_torus, and the average number of clouds along radial equatorial rays N_0. The viewing angle is not the only parameter controlling the classification of a galaxy into a type 1 or a type 2. In principle type 2s could be viewed at any viewing angle as long as there is one cloud along the line of sight. A more relevant quantity for clumpy media is the probability for an AGN photon to escape unabsorbed. In our sample, type 1s have relatively high escape probabilities, while in type 2s, as expected, tend to be low. Our fits also confirmed that the tori of Seyfert galaxies are compact with torus model radii in the range 1-6pc. The scaling of the models to the data also provided the AGN bolometric luminosities, which were found to be in good agreement with estimates from the literature. When we combined our sample of Seyfert galaxies with a sample of PG quasars from the literature to span a range of L_bol(AGN)~10^{43}-10^{47}erg/s, we found plausible evidence of the receding torus. That is, there is a tendency for the torus geometrical covering factor to be lower at high AGN luminosities than at low AGN luminosities. This is because at low AGN luminosities the tori appear to have wider angular sizes and more clouds along radial equatorial rays. We cannot, however rule out the possibility that this is due to contamination by extended dust structures not associated with the dusty torus at low AGN luminosities, since most of these in our sample are hosted in highly inclined galaxies. (Abridged)
Fitting the spectral energy distributions (SEDs) of galaxies is an almost universally used technique that has matured significantly in the last decade. Model predictions and fitting procedures have improved significantly over this time, attempting to
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