The dependence of AGN activity on stellar and halo mass in Semi-Analytic Models
Fabio Fontanot (1), Anna Pasquali (2), Gabriella De Lucia (1), Frank, C. van den Bosch (3), Rachel S. Somerville (4,5), Xi Kang (6,2) ((1), INAF-Osservatorio Astronomico di Trieste, (2) Max-Planck-Institute fuer, Astronomie, Heidelberg, (3) Department of Physics & Astronomy

TL;DR
This study compares predictions from four semi-analytical galaxy formation models regarding AGN activity dependence on stellar and halo mass, highlighting areas of agreement and discrepancy with observational data.
Contribution
It provides a detailed comparison of semi-analytical models' predictions with observations, focusing on AGN activity dependence on galaxy and halo mass.
Findings
Models reproduce overall galaxy activity distributions but differ from observations in radio source predictions.
Almost all >1e12 Msun halos are predicted to host bright radio sources, contrary to observations.
Radio brightness dependence on halo mass is overestimated in models.
Abstract
AGN feedback is believed to play an important role in shaping a variety of observed galaxy properties, as well as the evolution of their stellar masses and star formation rates. In particular, in the current theoretical paradigm of galaxy formation, AGN feedback is believed to play a crucial role in regulating the levels of activity in galaxies, in relatively massive halos at low redshift. Only in recent years, however, has detailed statistical information on the dependence of galaxy activity on stellar mass, parent halo mass and hierarchy has become available. In this paper, we compare the fractions of galaxies belonging to different activity classes (star-forming, AGN and radio active) with predictions from four different and independently developed semi-analytical models. We adopt empirical relations to convert physical properties into observables (H_alpha emission lines, OIII line…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
