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Published March 1, 2022 | Submitted
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Trinity I: Self-Consistently Modeling the Dark Matter Halo-Galaxy-Supermassive Black Hole Connection from z = 0−10

Abstract

We present Trinity, a flexible empirical model that self-consistently infers the statistical connection between dark matter haloes, galaxies, and supermassive black holes (SMBHs). Trinity is constrained by galaxy observables from 0 < z < 10 (galaxies' stellar mass functions, specific and cosmic SFRs, quenched fractions, and UV luminosity functions) and SMBH observables from 0 < z < 6.5 (quasar luminosity functions, quasar probability distribution functions, active black hole mass functions, local SMBH mass-bulge mass relations, and the observed SMBH mass distributions of high redshift bright quasars). The model includes full treatment of observational systematics (e.g., AGN obscuration and errors in stellar masses). From these data, Trinity infers the average SMBH mass, SMBH accretion rate, merger rate, and Eddington ratio distribution as functions of halo mass, galaxy stellar mass, and redshift. Key findings include: 1) the normalization of the SMBH mass-bulge mass relation increases only mildly from z = 0 to z = 3, but decreases more strongly from z = 3 to z = 10; 2) The AGN radiative+kinetic efficiency is ∼0.04-0.07, and does not show significant redshift dependence given the existing data constraints; 3) AGNs show downsizing, i.e., the Eddington ratios of more massive SMBHs start to decrease earlier than those of lower-mass objects; 4) The average ratio between average SMBH accretion rate and SFR is ∼10⁻³ for low-mass galaxies, which are primarily star-forming. This ratio increases to ∼10⁻¹ for the most massive haloes below z ∼ 1, where star formation is quenched but SMBHs continue to accrete.

Additional Information

We thank Stacey Alberts, Rachael Amaro, Gurtina Besla, Haley Bowden, Jane Bright, Katie Chamberlain, Alison Coil, Ryan Endsley, Sandy Faber, Dan Foreman-Mackey, Nico GaravitoCamargo, Nickolay Gnedin, Richard Green, Jenny Greene, Kate Grier, Melanie Habouzit, Kevin Hainline, Andrew Hearin, Julie Hlavacek-Larrondo, Luis Ho, Allison Hughes, Yun-Hsin Huang, Raphael Hviding, Victoria Jones, Stephanie Juneau, Ryan Keenan, David Koo, Andrey Kravtsov, Daniel Lawther, Rixin Li, Joseph Long, Jianwei Lyu, Chung-Pei Ma, Garreth Martin, Karen Olsen, Feryal Özel, Vasileios Paschalidis, Dimitrios Psaltis, Joel Primack, Yujing Qin, Eliot Quataert, George Rieke, Marcia Rieke, Jan-Torge Schindler, Spencer Scott, Xuejian Shen, Yue Shen, Dongdong Shi, Irene Shivaei, Rachel Somerville, Fengwu Sun, Wei-Leong Tee, Yoshihiro Ueda, Marianne Vestergaard, Feige Wang, Ben Weiner, Christina Williams, Charity Woodrum, Jiachuan Xu, Jinyi Yang, Minghao Yue, Dennis Zaritsky, Huanian Zhang, Xiaoshuai Zhang, and Zhanbo Zhang for very valuable discussions. Support for this research came partially via program number HST-AR-15631.001-A, provided through a grant from the Space Telescope Science Institute under NASA contract NAS5-26555. PB was partially funded by a Packard Fellowship, Grant #2019-69646. PB was also partially supported by a Giacconi Fellowship from the Space Telescope Science Institute. Finally, PB was also partially supported through program number HST-HF2-51353.001-A, provided by NASA through a Hubble Fellowship grant from the Space Telescope Science Institute, under NASA contract NAS5-26555. Data compilations from many studies used in this paper were made much more accurate and efficient by the online WEBPLOTDIGITIZER code. This research has made extensive use of the arXiv and NASA's Astrophysics Data System. This research used the Ocelote supercomputer of the University of Arizona. The allocation of computer time from the UA Research Computing High Performance Computing (HPC) at the University of Arizona is gratefully acknowledged. The Bolshoi-Planck simulation was performed by Anatoly Klypin within the Bolshoi project of the University of California High-Performance AstroComputing Center (UC-HiPACC; PI Joel Primack). DATA AVAILABILITY. The parallel implementation of TRINITY, the compiled datasets (§3.2), data for all figures, and the posterior distribution of model parameters (§4.1, Appendix G) are available online. https://github.com/HaowenZhang/TRINITY

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Additional details

Created:
August 20, 2023
Modified:
October 23, 2023