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arxiv: 2403.02404 · v2 · pith:HUHR66TH · submitted 2024-03-04 · astro-ph.HE · gr-qc

Impact of unmodeled eccentricity on the tidal deformability measurement and implications for gravitational wave physics inference

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classification astro-ph.HE gr-qc
keywords deformabilitytidaleccentricityinferenceerrorsmeasurementparameterfrequency
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With the expected large number of binary neutron star (BNS) observations through gravitational waves (GWs), third-generation GW detectors, Cosmic Explorer (CE) and Einstein Telescope (ET), will be able to constrain the tidal deformability, and hence the equation of state (EoS) of neutron star (NS) with exquisite precision. A subset of the detected BNS systems can retain residual eccentricity in the detector frequency band. We study the systematic errors due to unmodeled eccentricity in the tidal deformability measurement and its implications for NS EoS and redshift measurement via the Love siren method. We find that the systematic errors in the tidal deformability parameter exceed the statistical errors at an eccentricity of $\sim 10^{-3}$ ($\sim 3\times 10^{-4}$) at $10$Hz reference GW frequency for CE (ET). We show that these biases on tidal deformability parameter can significantly bias the NS EoS inference. Furthermore, the error on tidal deformability propagates to the source frame NS mass, which in turn biases the redshift inference. For CE, the redshift inference is significantly biased at an eccentricity of $\sim 10^{-3}$ (at a reference frequency of $10$Hz). We also study the implications of biased tidal deformability in testing the Kerr nature of black holes. Systematic error on the tidal deformability parameter leads to a non-zero value of tidal deformability for binary black holes, indicating a false deviation from the Kerr nature. Finally, we show that including eccentricity in the waveform model increases the statistical errors in tidal deformability measurement by a factor of $\lesssim 2$. Our study, therefore, highlights the importance of using accurate eccentric waveform models for GW parameter inference.

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