Accelerated orbital decay of supermassive black hole binaries in merging nuclear star clustersExport this event to calendar

Wednesday, November 13, 2019 — 11:15 AM EST

Astronomy Seminar Series

Go Ogiya

The coalescence of supermassive black holes (SMBHs) should generate the strongest sources of gravitational waves (GWs) in the Universe. However, the dynamics of their coalescence is the subject of much debate. In this study, we use a suite of N-body simulations to follow the merger of two nuclear star clusters (NSCs), each hosting a SMBH in their centre. We find that the presence of distinct star clusters around each SMBH has important consequences for the dynamical evolution of the SMBH binary: (i) The separation between the SMBHs decreases by a few orders of magnitude in the first few Myrs by the combined effects of dynamical friction and a drag force caused by tidally stripped stars. In fact, this is a significant speedup for equal mass ratio binaries, and becomes extreme for unequal mass ratios, e.g. 1:10 or 1:100, which traditional dynamical friction alone would not permit to bind. (ii) The subsequent binary hardening is driven by the gravitational slingshots between the SMBH binary and stars, and also depends on the mass ratio between the SMBHs. Thus, with this additional drag force, we find that all SMBHs in our suite coalesce within a Hubble time. Given that about 25% of low-mass galaxies host NSCs, our results are encouraging for upcoming GW observations with the Laser Interferometer Space Antenna - LISA - which will detect SMBH mergers in the 10^4-10^7 Msun mass range.


Go is a new WCA postdoctoral fellow. Before coming to Waterloo, he received his PhD degree from the University of Tsukuba (Japan) and held two postdoctoral positions at Max Planck Institute for extraterrestrial Physics (Germany) and Observatoire de la Côte d'Azur (France). His research interest spans a wide range of astrophysics, from the smallest dark matter halos to the largest galaxy clusters, and of numerical computation, including developing high-performance simulation codes and machine learning.

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