Outflows in the Discs of Active Galaxies

N. Menci, F. Fiore, C. Feruglio, A. Lamastra, F. Shankar, E. Piconcelli, E. Giallongo, A. Grazian

Published 2019-04-16Version 1

Recent advances in observations have provided a wealth of measurements of the expansions of outflows in galactic discs out to large radii in a variety of galactic hosts. To provide an updated baseline for the interpretation of such data, and to assess to what extent the present status of the modeling is consistent with the existing observations, we provide a compact two-dimensional description for the expansion of AGN-driven shocks in realistic galactic discs with exponential gas density profiles in a disc geometry. We derive solutions for the outflow expansion and the mass outflow rates in different directions with respect to the plane of the disc. These are expressed in terms of the global properties of the host galaxy and of the central AGN to allow for an easy and direct comparison with existing observations in a variety of galactic hosts with measured properties, and out to distances $\sim 10$ kpc from the centre. The results are compared with a state-of-the-art compilation of observed outflows in 19 galaxies with different measured gas and dynamical mass, allowing for a detailed, one-by-one comparison with the model predictions. The agreement we obtain for a wide range of host galaxy gas mass and AGN bolometric luminosity provides a quantitative systematic test for the modeling of AGN-driven outflows in galactic discs. We also consider a larger sample of galaxies with no reliable measurements of the gas and dynamical mass. In this case we perform a comparison of the model predictions for different bins of AGN luminosities assuming values for the gas mass and dynamical mass derived from scaling relations. The encouraging, quantitative agreement of the model predictions with a wide set of existing observations constitutes a baseline for the interpretation of forthcoming data, and for a more detailed treatment of AGN feedback in galaxy formation models.