Search for dynamical black hole captures with Gaussian mixture modelling
Leigh Smith, Shubhanshu Tiwari, Michael Ebersold, Yeong-Bok Bae, Gungwon Kang, Ik Siong Heng

TL;DR
This paper develops a Gaussian mixture modeling approach to enhance the detection of dynamical black hole capture events in gravitational wave data, improving sensitivity and setting new upper limits on event rates.
Contribution
It introduces a GMM-based method integrated with the cWB algorithm for detecting dynamical black hole captures, with applications to both generic and population-informed searches.
Findings
Informed GMM approach improves detection sensitivity up to 1.9 Gpc.
No new events found, but upper rate limits are tightened.
Detection of GW190521-like events estimated at 0.94 Gpc$^{-3}$yr$^{-1}$.
Abstract
Gravitational waves (GWs) are expected to originate from black holes interacting dynamically in dense astrophysical environments. In such environments, given that the velocity and cross section between the interacting black holes is low, dynamical capture may occur. Such events merge on very short timescales with high eccentricities and are expected to be detectable in the LIGO-Virgo-KAGRA (LVK) sensitivity band. In this work, we present a dedicated search for dynamical black hole capture events in the third LVK observing run with the coherent WaveBurst (cWB) algorithm enhanced with Gaussian mixture modeling (GMM) post-production. With this we consider two applications of GMM: a weakly-modeled approach searching for generic short transients under minimal assumptions, and a population informed approach, in which the GMM model is provided information on the parameter space occupied by the…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Astrophysical Phenomena and Observations · Gamma-ray bursts and supernovae
