Following massive black hole binaries and triplets to coalescence in live galaxy simulations: gravitational wave source predictions with RAMCOAL
Tsung-Dao Lee Institute/S4F-SW - Open Area
Tsung-Dao Lee Institute
Host: Yosuke Mizuno
Join Tencent Meeting:https://meeting.tencent.com/dm/cmRIOgbDA1a4
Meeting ID: 747482052 (no password)
Abstract:
Low-frequency gravitational-wave surveys need not just how often massive black holes (MBHs) merge, but the parameters carried to coalescence: masses, mass ratio, spins, eccentricity, and the delay since galaxy assembly. These depend on dynamical regimes that cosmological galaxy simulations cannot resolve. We extend RAMCOAL, a subgrid model in the RAMSES hydrodynamical code, to follow MBH binaries and now triplets — from galactic scales down to coalescence during the simulation. Black holes evolve from sink particles through a dynamical-friction phase into bound binaries hardened by stellar scattering, gas torques, circumbinary-disc coupling, and gravitational-wave emission, all within the live, evolving galaxy. When a later merger delivers a third MBH that drives the system chaotic, the model maps the interaction onto a library of three-body outcomes and updates the surviving binary, allowing exchanges, recoils, and ejections. Using isolated-galaxy tests, we show that the encounter geometry alone can change which pair finally coalesces, the eccentricity carried into the gravitational-wave band, and the merger delay (~0.3 to ~3.5 Gyr in our two configurations). By keeping coalescence, accretion, spin, and recoil coupled to the host galaxy, RAMCOAL aims to provide pre-coalescence source properties and electromagnetic context for multimessenger predictions.
Biography:
Kunyang Li (李坤阳)joined the Center for Computational Astrophysics (CCA) as a Flatiron Research Fellow in 2025. Her work focuses on modelling the evolution and realistic coalescence processes of massive black holes "on-the-fly" in cosmological simulations and on developing optimal strategies for detecting GW counterparts through EM searches.
