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arXiv · 2508.00481

Search for dynamical black hole captures with Gaussian mixture modelling

Leigh Smith·Shubhanshu Tiwari·Michael Ebersold·Yeong-Bok Bae·Gungwon Kang·Ik Siong Heng
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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 capture population. Although our search does not find any new significant GW events, we find that an informed GMM approach brings significant sensitivity improvements, enabling the detection of dynamical capture events up to a distance of 1.9 Gpc for a 200 $M_{\odot}$ equal mass binary. We present updated upper limit estimates of the rate at 90\% confidence, the most stringent of which is 0.15 Gpc$^{-3}$yr$^{-1}$, a 34\% improvement with respect to previous observational estimates. Furthermore, while the weakly-modeled GMM approach is less sensitive to dynamical capture systems, we find that it is possible for these events to be detected up to a distance of 1 Gpc in the cWB-GMM all-sky short search under minimal assumptions. Finally, with the confident detection of GW190521, we estimate the rate of similar events to be 0.94 Gpc$^{-3}$yr$^{-1}$, assuming the event originated from a dynamical capture.

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Leigh Smith, Shubhanshu Tiwari, Michael Ebersold, Yeong-Bok Bae, Gungwon Kang, Ik Siong Heng. 2025-08-01. Search for dynamical black hole captures with Gaussian mixture modelling. https://arxiv.org/abs/2508.00481

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