Inferring eccentricity evolution from observations of coalescing binary black holes
Alice Bonino, Rossella Gamba, Patricia Schmidt, Alessandro Nagar, Geraint Pratten, Matteo Breschi, Piero Rettegno, Sebastiano Bernuzzi
Preprint on arxiv:2207.10474 [gr-qc]
Published in Phys. Rev. D 107 (2023) 064024
Published:
The origin and formation of stellar-mass binary black holes remains an open question that can be addressed by precise measurements of the binary and orbital parameters from their gravitational-wave signal. Such binaries are expected to circularize due to the emission of gravitational waves as they approach merger. However, depending on their formation channel, some binaries could have a non-negligible eccentricity when entering the frequency band of current gravitational-wave detectors. In order to measure eccentricity in an observed gravitational-wave signal, accurate waveform models that describe binaries in eccentric orbits are necessary. In this work we demonstrate the efficacy of the improved TEOBResumS waveform model for eccentric coalescing binaries with aligned spins. We first validate the model against mock signals of aligned-spin binary black hole mergers and quantify the impact of eccentricity on the estimation of other intrinsic binary parameters. We then perform a fully Bayesian reanalysis of GW150914 with the eccentric waveform model. We find (i) that the model is reliable for aligned-spin binary black holes and (ii) that GW150914 is consistent with a non-eccentric merger although we cannot rule out small values of initial eccentricity at a reference frequency of 20 Hz. Finally, we present a systematic method to measure the eccentricity and its evolution directly from the gravitational-wave posterior samples. Such an estimator is useful when comparing results from different analyses as the definition of eccentricity may differ between models. Our scheme can be applied even in the case of small eccentricities and can be adopted straightforwardly in post-processing to allow for direct comparison between models.