The $M_\bullet$-$σ_e$ relation for local type 1 AGNs and quasars
Abstract: We analyzed MUSE observations of 42 local $z<0.1$ type 1 active galactic nucleus (AGN) host galaxies taken from the Palomar-Green quasar sample and the close AGN reference survey. Our goal was to study the relation between the black hole mass ($M_\bullet$) and bulge stellar velocity dispersion ($\sigma_e$) for type 1 active galaxies. The sample spans black hole masses of $10{6.0}-10{9.2}\,M_\odot$, bolometric luminosities of $10{42.9}-10{46.0}\,$erg$\,$s${-1}$, and Eddington ratios of 0.006-1.2. We avoided AGN emission by extracting the spectra over annular apertures. We modeled the calcium triplet stellar features and measured stellar velocity dispersions of $\sigma_* = 60-230\,$km$\,$s${-1}$ for the host galaxies. We find $\sigma_$ values in agreement with previous measurements for local AGN host galaxies, but slightly lower compared with those reported for nearby X-ray-selected type 2 quasars. Using a novel annular aperture correction recipe to estimate $\sigma_e$ from $\sigma_$ that considers the bulge morphology and observation beam-smearing, we estimate flux-weighted $\sigma_e = 60-250\,$km$\,$s${-1}$. If we consider the bulge type when estimating $M_\bullet$, we find no statistical difference between the distributions of AGN hosts and the inactive galaxies on the $M_\bullet - \sigma_e$ plane for $M_\bullet \lesssim 108\,M_\odot$. Conversely, if we do not consider the bulge type when computing $M_\bullet$, we find that both distributions disagree. We find no correlation between the degree of offset from the $M_\bullet - \sigma_e$ relation and Eddington ratio for $M_\bullet \lesssim 108\,M_\odot$. The current statistics preclude firm conclusions from being drawn for the high-mass range. We argue these observations support notions that a significant fraction of the local type 1 AGNs and quasars have undermassive black holes compared with their host galaxy bulge properties.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.