Obscured Active Galactic Nuclei triggered in compact star-forming galaxies

Abstract

We present a structural study of 182 obscured Active Galactic Nuclei (AGNs) at z<=1.5, selected in the COSMOS field from their extreme infrared to X-ray luminosity ratio and their negligible emission at optical wavelengths. We fit optical to far-infrared spectral energy distributions and analyze deep HST imaging to derive the physical and morphological properties of their host galaxies. We find that such galaxies are more compact than normal star-forming sources at similar redshift and stellar mass, and we show that it is not an observational bias related to the emission of the AGN. Based on the distribution of their UVJ colors, we also argue that this increased compactness is not due to the additional contribution of a passive bulge. We thus postulate that a vast majority of obscured AGNs reside in galaxies undergoing dynamical compaction, similar to processes recently invoked to explain the formation of compact star-forming sources at high redshift.

Accepted for publication in MNRAS Letter

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Cited by in corpus (11)

• The Size Evolution of Star-forming and Quenched Galaxies in the IllustrisTNG simulation
• No significant evolution of relations between Black hole mass and Galaxy total stellar mass up to z~2.5
• Infrared Selection of Obscured Active Galactic Nuclei in the COSMOS Field
• The Chandra Deep Fields: Lifting the Veil on Distant Active Galactic Nuclei and X-Ray Emitting Galaxies
• ALMA Twenty-six arcmin${}^2$ Survey of GOODS-S at One-millimeter (ASAGAO): X-ray AGN Properties of Millimeter-Selected Galaxies
• The Evolving Interstellar Medium of Star-Forming Galaxies, as traced by $\text{Stardust}$
• The role of the most luminous, obscured AGN in galaxy assembly at z~2
• A census of radio-selected AGN on the COSMOS field and of their FIR properties
• Unveiling Sizes of Compact AGN Hosts with ALMA
• The effect of the morphological quenching mechanism on star formation activity at 0.5 < z < 1.5 in 3D-HST/CANDELS
• Identifying AGN host galaxies by Machine Learning with HSC+WISE