Marginalised parameters of the wabs (top), wabs+scattering, sphere+pexmon+scattering and wabs+pexmon+scattering model (bottom) for source 179. The posterior probability density distribution, normalised to the maximum, is shown by grey bars. The blue line indicates the cumulative posterior distribution. For summary of the error, the median and 10/90% quantiles can be used, or as the blue error bar indicates, the 1 standard-deviation equivalent probabilities.

Evidence contribution from each source with secure spectroscopic redshift. The vertical axis shows the Bayes factor between torus+pexmon+scattering and wabs+pexmon+scattering (red circles), where strong preference for the torus is above log  10 = 1. The same is shown for sphere+pexmon+scattering and wabs+pexmon+scattering (black squares). In both model comparisons, there are obscured objects showing significant preference for either model.

Histograms of the best parameter values derived using the torus+pexmon+scattering model. The median of the marginal posterior distribution for each object is histogrammed in black. The thick red line shows the same as a cumulative distribution. To illustrate the uncertainty in the parameters, the dotted red lines show the cumulative distribution of the 10% and 90% quantiles instead of the median. The dashed gray line shows the used prior.

Comparison of the derived column density (left panels, N_H, here in logarithmic) and intrinsic luminosity (right panel, logarithmic, in erg / s for the 2−10 keV rest frame band) with the analysis of Tozzi et al. (2006). We selected only objects from our sample which have the same redshift in Tozzi et al. (2006) and this work. We plot the median and 1-sigma equivalent quantiles of the posterior in our analysis against the best fit found in Tozzi et al. (2006). There are important differences between the works. The Tozzi et al. (2006) analysis is based on only the first 1Ms data, and thus has much fewer counts. Furthermore, only simple absorption models have been considered in their maximum likelihood fitting.