Reconstructing the Genealogy of LIGO-Virgo Black Holes
Parthapratim Mahapatra, Debatri Chattopadhyay, Anuradha Gupta, Fabio, Antonini, Marc Favata, B. S. Sathyaprakash, and K. G. Arun

TL;DR
This paper introduces a Bayesian inference method to trace the hierarchical merger history of binary black holes detected by LIGO-Virgo, especially those in the pair-instability mass gap, providing insights into their astrophysical origins.
Contribution
The paper presents a novel Bayesian framework that reconstructs the merger history of black holes using numerical relativity predictions, validated through injection studies and applied to real GW events.
Findings
Hierarchical mergers can explain black holes in the mass gap.
The method estimates parent binary parameters with credible intervals.
Astrophysical environments with high escape speeds are favored for these events.
Abstract
We propose a Bayesian inference framework to predict the merger history of LIGO-Virgo binary black holes (BHs), whose binary components may have undergone hierarchical mergers in the past. The framework relies on numerical relativity predictions for the mass, spin, and kick velocity of the remnant BHs. This proposed framework computes the masses, spins, and kicks imparted to the remnant of the parent binaries, given the initial masses and spin magnitudes of the binary constituents. We validate our approach by performing an ``injection study'' based on a constructed sequence of hierarchically formed binaries. Noise is added to the final binary in the sequence, and the parameters of the `parent' and `grandparent' binaries in the merger chain are then reconstructed. This method is then applied to three GWTC-3 events: GW190521, GW200220_061928, and GW190426_190642. These events were…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Relativity and Gravitational Theory · Astrophysical Phenomena and Observations
