1 mars 2021 à 9 avril 2021
Institut Henri Poincaré
Fuseau horaire Europe/Paris

Eccentric binary black hole surrogate models for the gravitational waveform andremnant properties: comparable mass, nonspinning case

Non programmé
20m
Amphitheater Darboux (Institut Henri Poincaré)

Amphitheater Darboux

Institut Henri Poincaré

11, Rue Pierre et Marie Curie 75005 Paris
Poster

Description

We develop new strategies to build numerical relativity surrogate models for
eccentric binary black hole systems, which are expected to play an increasingly
important role in current and future gravitational-wave detectors. We
introduce a new surrogate waveform model, NRSur2dq1Ecc, using 47
nonspinning, equal-mass waveforms with eccentricities up to $0.2$ when measured
at a reference time of $5500M$ before merger. This is the first waveform model
that is directly trained on eccentric numerical relativity simulations and does
not require that the binary circularizes before merger. The model includes the
$(2,2)$, $(3,2)$, and $(4,4)$ spin-weighted spherical harmonic modes. We also
build a final black hole model, NRSur2dq1EccRemnant, which models the
mass, and spin of the remnant black hole. We show that our waveform model can
accurately predict numerical relativity waveforms with mismatches $\approx 10^{-3}$, while the remnant model can recover the final mass and dimensionless
spin with absolute errors smaller than $\approx 5 \times 10^{-4}M$ and
$\approx 2 \times10^{-3}$ respectively. We demonstrate that the waveform model
can also recover subtle effects like mode-mixing in the ringdown signal without
any special ad-hoc modeling steps. Finally, we show that despite being trained
only on equal-mass binaries, NRSur2dq1Ecc can be reasonably extended
up to mass ratio $q\approx3$ with mismatches $\simeq 10^{-2}$ for
eccentricities smaller than $\sim 0.05$ as measured at a reference time of
$2000M$ before merger. The methods developed here should prove useful in the
building of future eccentric surrogate models over larger regions of the
parameter space.

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