Journal article Open Access

# Forward modeling the multiwavelength properties of Active Galactic Nuclei: application to X-ray and WISE mid-infrared samples

Antonis Georgakakis; Stephanie LaMassa; Angel Ruiz

An empirical forward-modeling framework is developed to interpret the multi-wavelength properties of Active Galactic Nuclei (AGN) and provide insights into the overlap and incompleteness of samples selected at different parts of the electromagnetic spectrum. The core of the model are observationally derived probabilites on the occupation of galaxies by X-ray selected AGN. These are used to seed mock galaxies drawn from stellar-mass functions with accretion events and then associate them with spectral energy distributions that describe both the stellar and AGN emission components. This approach is used to study the complementarity between X-ray and WISE mid-infrared  AGN selection methods. We first show that the basic observational properties of the X-ray and WISE AGN (magnitude, redshift distributions) are adequately reproduced by the model. We then infer the level of contamination of the WISE selection and show that this is dominated by non-AGN at redshifts $z<0.5$. These are star-forming galaxies that scatter into the WISE AGN selection wedge because of photometric uncertainties affecting their colours. Our baseline model shows a sharp drop in the number density of heavily obscured AGN above the Compton thick limit in the WISE bands. The model also overpredicts by a factor of 1.5 the fraction of X-ray associations in the WISE AGN selection box compared to observations. This suggests a population of X-ray faint sources that is not reproduced by the model. This discrepancy is discussed in the context of either heavily obscured or intrinsically X-ray weak AGN. Evidence is found in favour of the latter.

The two mock AGN/galaxy catalogues used in the paper are made available. The contents are described in the README file. The demo.py script shows how the mock catalogues can be used to select AGN in the Assef+13 WISE R75 selection wedge.  Full paper at https://arxiv.org/abs/2009.00060

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demo.py
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md5:ba849355bd4f6f622ec288562fbf26a2
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SIM.fits
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SIM_BASELINE.fits
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