Spectral Analysis Using BayesWave for Characterizing Remnant Outcomes in Binary Neutron Star Mergers

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Abstract:: Neutron stars provide key insights into extreme stellar physics, with the detection of GW170817 marking the start of multimessenger astronomy by revealing both gravitational and electromagnetic signals. However, current gravitational wave detectors struggle to capture the postmerger phase, which holds crucial information about the remnant's characteristics and the nuclear equation of state (EoS). This research investigates the postmerger phase of binary neutron star (BNS) mergers using the morphology-agnostic BayesWave CPP algorithm, where we employed sine-Gaussian wavelets for adaptive signal reconstruction. By analyzing simulated signals of stiff and soft EoSs informed by numerical relativity at Cosmic Explorer (CE) sensitivity, we distinguished between long-lived neutron star remnants and those that promptly collapse into black holes. BayesWave CPP proved efficient in reconstructing the signals, accurately capturing the dominant and subdominant gravitational wave frequency modes at CE sensitivity. Additionally, in the promptly collapsing cases, we found that high-frequency oscillations identified in numerical simulations are associated with quasiradial stellar oscillations, rather than black hole ringdown quasinormal modes. Overall, this research highlights the effectiveness of BayesWave CPP for postmerger signal reconstruction and its potential to tackle the complex physics of BNS mergers and their remnants.

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LIGO Document P2400354-v1