Leveraging NRTidalv3 to develop gravitational waveform models with higher-order modes for binary neutron star systems
Adrian Abac, Felip A. Ramis Vidal, Marta Colleoni, Anna Puecher, Alejandra Gonzalez, Tim Dietrich
TL;DR
This work extends the NRTidalv3 tidal model to include higher-order mode corrections for binary neutron star systems and couples it to leading BBH baselines (e.g., IMRPhenomXHM, SEOBNRv5HM_ROM), enabling more accurate HM-inclusive waveforms. The HM-NRTidalv3 models are validated against numerical-relativity simulations in both time and frequency domains, showing improved agreement over HM-free counterparts, with manageable computational cost. Parameter-estimation studies demonstrate robust recovery for comparable-mass systems but reveal degeneracies among mass and spin for high-mass-ratio cases, mitigated by incorporating precession. Reanalysis of GW170817 with HM models yields results consistent with prior analyses, highlighting the practical relevance for future detections and the potential for NSBH modeling and broader EOS exploration.
Abstract
Accurate and reliable gravitational waveform models are crucial in determining the properties of compact binary mergers. In particular, next-generation gravitational-wave detectors will require more accurate waveforms to avoid biases in the analysis. In this work, we extend the recent NRTidalv3 model to account for higher-mode corrections in the tidal phase contributions for binary neutron star systems. The higher-mode, multipolar NRTidalv3 model is then attached to several binary-black-hole baselines, such as the phenomenological IMRPhenomXHM and IMRPhenomXPHM models, and the effective-one-body-based model SEOBNRv5HM_ROM. We test the performance and validity of the newly developed models by comparing them with numerical-relativity simulations and other tidal models. Finally, we employ them in parameter estimation analyses on simulated signals from both comparable-mass and high-mass-ratio systems, as well as on the gravitational-wave event GW170817, for which we find consistent results with respect to previous analyses.
