The XMM-Newton Wide field survey in the COSMOS field: redshift evolution of AGN bias and subdominant role of mergers in triggering moderate luminosity AGN at redshift up to 2.2
V. Allevato, A. Finoguenov, N. Cappelluti, T. Miyaji, G. Hasinger, M., Salvato, M. Brusa, R. Gilli, G. Zamorani, F. Shankar, J. B. James, H. J., McCracken, A. Bongiorno, A. Merloni, J. A. Peacock, J. Silverman, A., Comastri

TL;DR
This study investigates the evolution of AGN clustering and bias up to redshift 2.2, revealing that major mergers are not the dominant trigger for moderate luminosity AGN, with implications for understanding galaxy evolution.
Contribution
Introduces a new method to estimate AGN bias and host halo mass evolution, demonstrating the limited role of mergers in triggering moderate luminosity AGN up to z=2.2.
Findings
AGN bias increases with redshift, indicating more clustered AGN at higher z.
Host halo mass remains roughly constant (~10^13 h^-1 M_sun) across redshifts.
Major mergers do not fully explain the observed AGN clustering, suggesting other triggering mechanisms.
Abstract
We present a study of the redshift evolution of the projected correlation function of 593 X-ray selected AGN with I_AB<23 and spectroscopic redshifts z<4, extracted from the 0.5-2 keV X-ray mosaic of the 2.13 deg^2 XMM-COSMOS survey. We introduce a method to estimate the average bias of the AGN sample and the mass of AGN hosting halos, solving the sample variance using the halo model and taking into account the growth of the structure over time. We find evidence of a redshift evolution of the bias factor for the total population of XMM-COSMOS AGN from b(z=0.92)=2.30 +/- 0.11 to b(z=1.94)=4.37 +/- 0.27 with an average mass of the hosting DM halos logM [h^-1 M_sun] ~ 13.12 +/- 0.12 that remains constant at all z < 2. Splitting our sample into broad optical lines AGN (BL), AGN without broad optical lines (NL) and X-ray unobscured and obscured AGN, we observe an increase of the bias with…
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.
