VLT/SINFONI study of black hole growth in high redshift radio-loud quasars from the CARLA survey
M. Marinello, R.A. Overzier, H.J.A. R\"ottgering, J.D. Kurk, C. De, Breuck, J. Vernet, D. Wylezalek, D. Stern, K.J. Duncan, N. Hatch, N., Kashikawa, Y.-T. Lin, R.S. Nemmen, A. Saxena

TL;DR
This study uses VLT/SINFONI observations to analyze black hole growth in high-redshift radio-loud quasars from the CARLA survey, revealing insights into black hole mass estimates, radio power correlations, and growth histories.
Contribution
It provides new Ha-based black hole mass estimates, compares them with CIV-based estimates, and explores the relationship between black hole properties, radio emission, and environment in high-redshift quasars.
Findings
CIV-based masses have a scatter of 0.35 dex compared to Ha-based masses.
Correcting CIV-based masses reduces scatter to 0.24 dex.
Weak correlation between radio power and black hole properties, but strong inverse correlation between CIV blueshift and radio power.
Abstract
We present VLT/SINFONI observations of 35 quasars at 2.1 < z < 3.2, the majority of which were selected from the Clusters Around Radio-Loud AGN (CARLA) survey. CARLA quasars have large CIV-based black hole (BH) masses (M(BH) > 10^9 Msun) and powerful radio emission (P(500MHz) > 27.5 W/Hz). We estimate Ha-based M(BH), finding a scatter of 0.35 dex compared to CIV. We evaluate several recipes for correcting CIV-based masses, which reduce the scatter to 0.24 dex. The radio power of the radio-loud quasars is at most weakly correlated with the interconnected quantities Ha-width, L(5100A) and M(BH), suggesting that it is governed by different physical processes. However, we do find a strong inverse correlation between CIV blueshift and radio power linked to higher Eddington ratios and L(5100A). Under standard assumptions, the BH growth time is longer than the cosmic age for many CARLA…
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.
