Title: Spin Doctors: How to Diagnose a Hierarchical Merger Origin
Abstract Gravitational-wave observations provide the unique opportunity of studying black hole formation channels and histories—but only if we can identify their origin. One such formation mechanism is the dynamical synthesis of black hole binaries in dense stellar systems. Given the expected isotropic distribution of component spins of binary black holes in gas-free dynamical environments, the presence of antialigned or in-plane spins with respect to the orbital angular momentum is considered a tell-tale sign of a merger’s dynamical origin. Even in the scenario where birth spins of black holes are low, hierarchical mergers attain large component spins due to the orbital angular momentum of the prior merger. However, measuring such spin configurations is difficult. Here, we quantify the efficacy of the spin parameters encoding aligned-spin (χeff) and in-plane spin (χp) at classifying such hierarchical systems. Using Monte Carlo cluster simulations to generate a realistic distribution of hierarchical merger parameters from globular clusters, we can infer mergers’χeffandχp. The cluster populations are simulated using Advanced LIGO-Virgo sensitivity during the detector network’s third observing period and projections for design sensitivity. Using a “likelihood-ratio”-based statistic, we find that ∼2% of the recovered population by the current gravitational-wave detector network has a statistically significantχpmeasurement, whereas noχeffmeasurement was capable of confidently determining a system to be antialigned with the orbital angular momentum at current detector sensitivities. These results indicate that measuring spin-precession throughχpis a more detectable signature of hierarchical mergers and dynamical formation than antialigned spins.  more » « less
Award ID(s):
2207945
PAR ID:
10522308
Author(s) / Creator(s):
; ;
Publisher / Repository:
The Astrophysical Journal Letters
Date Published:
Journal Name:
The Astrophysical Journal Letters
Volume:
966
Issue:
1
ISSN:
2041-8205
Page Range / eLocation ID:
L16
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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