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High-redshift supermassive black hole mergers in simulations with dynamical friction modelling.

Authors :
DeGraf, Colin
Chen, Nianyi
Ni, Yueying
Di Matteo, Tiziana
Bird, Simeon
Tremmel, Michael
Croft, Rupert
Source :
Monthly Notices of the Royal Astronomical Society. Feb2024, Vol. 527 Issue 4, p11766-11776. 11p.
Publication Year :
2024

Abstract

In the near future, projects like Laser Interferometer Space Antenna (LISA) and pulsar timing arrays are expected to detect gravitational waves from mergers between supermassive black holes, and it is crucial to precisely model the underlying merger populations now to maximize what we can learn from this new data. Here, we characterize expected high-redshift (z > 2) black hole mergers using the very large volume Astrid cosmological simulation, which uses a range of seed masses to probe down to low-mass black holes (BHs), and directly incorporates dynamical friction so as to accurately model the dynamical processes that bring black holes to the galaxy centre where binary formation and coalescence will occur. The black hole populations in Astrid include black holes down to |$\sim 10^{4.5} \, \mathrm{M}_\odot$|⁠ , and remain broadly consistent with the TNG simulations at scales |$\gt 10^6 \, \mathrm{M}_\odot$| (the seed mass used in TNG). By resolving lower mass black holes, the overall merger rate is ∼5× higher than in TNG. However, incorporating dynamical friction delays mergers compared to a recentring scheme, reducing the high- z merger rate mass-matched mergers by a factor of ∼2×. We also calculate the expected LISA signal-to-noise values, and show that the distribution peaks at high SNR (>100), emphasizing the importance of implementing a seed mass well below LISA 's peak sensitivity (⁠|$\sim 10^6 \, \mathrm{M}_\odot$|⁠) to resolve the majority of LISA 's gravitational wave detections. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00358711
Volume :
527
Issue :
4
Database :
Academic Search Index
Journal :
Monthly Notices of the Royal Astronomical Society
Publication Type :
Academic Journal
Accession number :
175010890
Full Text :
https://doi.org/10.1093/mnras/stad3084