Physical Processes Behind the Co-Evolution of Halos, Galaxies and Supermassive Black Holes in the IllustrisTNG Simulation

TL;DR

This paper investigates the co-evolution of dark matter halos, galaxies, and supermassive black holes in the IllustrisTNG simulation, identifying distinct evolutionary phases and the processes driving transitions between them.

Contribution

It provides a detailed phase-based framework for understanding galaxy and black hole co-evolution, highlighting the roles of feedback mechanisms and mergers.

Findings

Four distinct evolutionary phases identified in galaxy-black hole co-evolution.

Transitions driven by AGN feedback modes and gas unbinding.

Scaling relations help interpret simulation processes and guide observations.

Abstract

We explore the co-evolution of dark matter halos, their central galaxies, and central supermassive black holes (SMBHs) using the IllustrisTNG (TNG) simulation. We find that the evolutionary histories of individual galaxies in the $M_{\rm BH}$-$M_*$ plane can be decomposed into four distinct phases, separated by three transition points. We identify the driving processes of galaxy evolution within each phase and derive the conditions necessary and sufficient for transitions to subsequent phases. The first phase is dominated by star formation, with its duration primarily determined by the mass of the SMBH seed and the surrounding gas environment. The second phase is characterized by rapid SMBH growth, and the transition to the next phase occurs when the thermal-mode feedback of active galactic nucleus (AGN) can unbind gas from the galaxy. The third phase involves self-regulation of the SMBH, and the transition to the quenched phase occurs when the kinetic-mode feedback of AGN counterbalances gas cooling within the subhalo. The final phase is dominated by mergers. We investigate the use of scaling relations among different mass components and evolutionary phases to understand processes implemented in TNG and other simulations, and discuss how current and forthcoming observations can be used to constrain models.