Speaker
Description
In this study, we focus on the high frequency gravitational-wave emission from the hyper-massive neutron star temporarily formed after a binary neutron star (BNS) merger. We evaluate how accurately KAGRA can estimate the post-merger peak frequency $f_{\mathrm{peak}}$ using the BayesWave analysis on numerical-relativity waveform models injected into Gaussian noise colored by the detector’s PSD. Since $f_{\mathrm{peak}}$ strongly depends on the dense matter equation of state, its estimation is crucial for placing physical constraints. Using the planned future KAGRA sensitivity optimized for the high frequency band, we assess KAGRA’s potential to constrain neutron star physics for BNS signals at various distances. Our results indicate that a signal to noise ratio of approximately 6–7 in the post-merger band is required for BayesWave to reliably reconstruct $f_{\mathrm{peak}}$ for EOS discrimination.
