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arXiv · 1501.00966

Coherent Network Analysis of Gravitational Waves from Three-Dimensional Core-Collapse Supernova Models

Abstract

Using predictions from three-dimensional (3D) hydrodynamics simulations of core-collapse supernovae (CCSNe), we present a coherent network analysis to detection, reconstruction, and the source localization of the gravitational-wave (GW) signals. We use the {\tt RIDGE} pipeline for the analysis, in which the network of LIGO Hanford, LIGO Livingston, VIRGO, and KAGRA is considered. By combining with a GW spectrogram analysis, we show that several important hydrodynamics features in the original waveforms persist in the waveforms of the reconstructed signals. The characteristic excess in the spectrograms originates not only from rotating core-collapse, bounce and the subsequent ring down of the proto-neutron star (PNS) as previously identified, but also from the formation of magnetohydrodynamics jets and non-axisymmetric instabilities in the vicinity of the PNS. Regarding the GW signals emitted near at the rotating core bounce, the horizon distance extends up to $\sim$ 18 kpc for the most rapidly rotating 3D model in this work. Following the rotating core bounce, the dominant source of the GW emission shifts to the non-axisymmetric instabilities. The horizon distances extend maximally up to $\sim$ 40 kpc seen from the spin axis. With an increasing number of 3D models trending towards explosion recently, our results suggest that in addition to the best studied GW signals due to rotating core-collapse and bounce, the time is ripe to consider how we can do science from GWs of CCSNe much more seriously than before. Particularly the quasi-periodic signals due to the non-axisymmetric instabilities and the detectability should deserve further investigation to elucidate the inner-working of the rapidly rotating CCSNe.

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BibTeXRIS

Kazuhiro Hayama, Takami Kuroda, Kei Kotake, Tomoya Takiwaki. 2015-11-16. Coherent Network Analysis of Gravitational Waves from Three-Dimensional Core-Collapse Supernova Models. https://doi.org/10.1103/physrevd.92.122001

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