Reanalysis of the binary neutron star mergers GW170817 and GW190425 using numerical-relativity calibrated waveform models

TL;DR

This study reanalyzes gravitational wave data from two neutron star mergers using advanced waveform models, highlighting the impact of waveform choice on tidal deformability estimates and emphasizing the importance of systematic uncertainties.

Contribution

It introduces a NR-calibrated waveform model with nonlinear tidal terms and compares its results with other models, assessing model dependence in parameter estimation.

Findings

Estimates of tidal deformability depend on waveform models.

Systematic differences are smaller than statistical errors for current data.

Model dependence will become more significant with future detector sensitivities.

Abstract

We reanalyze gravitational waves from binary-neutron-star mergers GW170817 and GW190425 using a numerical-relativity (NR) calibrated waveform model, the TF2+_Kyoto model, which includes nonlinear tidal terms. For GW170817, by imposing a uniform prior on the binary tidal deformability $\tilde{\Lambda}$, the symmetric $90\%$ credible interval of $\tilde{\Lambda}$ is estimated to be $481^{+436}_{-359}$ and $402^{+465}_{-279}$ for the case of $f_\mathrm{max}=1000$ and $2048~\mathrm{Hz}$, respectively, where $f_\mathrm{max}$ is the maximum frequency in the analysis. We also reanalyze the event with other waveform models: two post-Newtonian waveform models (TF2_PNTidal and TF2+_PNTidal), the TF2+_NRTidal model that is another NR calibrated waveform model, and its upgrade, the TF2+_NRTidalv2 model. While estimates of parameters other than $\tilde{\Lambda}$ are broadly consistent among various waveform models, our results indicate that estimates of $\tilde{\Lambda}$ depend on waveform models. However, the difference is smaller than the statistical error. For GW190425, we can only obtain little information on the binary tidal deformability. The systematic difference among the NR calibrated waveform models will become significant to measure $\tilde{\Lambda}$ as the number of detectors and events increase and sensitivities of detectors are improved.