3 years ago

Gravitational wave spectroscopy of binary neutron star merger remnants with mode stacking.

Frans Pretorius, Vasileios Paschalidis, Luis Lehner, Nicolas Yunes, Kent Yagi, Huan Yang

A binary neutron star (BNS) merger event has recently been observed in gravitational waves (GWs). As in the case of binary black holes, GWs generated by BNS consist of inspiral, merger, and post-merger components. Detecting the latter is important because it encodes information about the nuclear equation of state (EOS) in a regime that cannot be probed prior to merger. The post-merger signal, however, can be observed by current detectors only out to~ 10 Mpc. We carry out Monte-Carlo simulations showing that the dominant post-merger signal (the 22 mode) from individual events will likely not be observable even with the Einstein Telescope and Cosmic Explorer (CE), assuming a full year of operation, the latest merger rates, and a detection threshold with signal-to-noise ratio of 5. For this reason, we propose two methods that stack the post-merger signal from multiple events to boost the detection probability. The first method follows a commonly-used practice of multiplying the Bayes factors of individual events. The second method relies on an assumption that the mode phase can be determined from the inspiral waveform, so that coherent mode stacking of the data from different events becomes possible. Both methods significantly improve the chances of detecting the dominant post-merger signal, making a detection very likely after a year of observation with CE for certain EOS. We also show that in terms of detection, coherent stacking is more efficient in accumulating confidence. Moreover, assuming a 22 mode is detected with CE via stacking, we estimate through a Fisher analysis that the peak frequency can be measured to a statistical error of ~ 4-20 Hz for certain equations of state. Such an error corresponds to a NS radius measurement of ~ 15-56 m, a fractional relative error ~ 4 %, suggesting that systematic errors from theoretical modeling (~ 100 m) may dominate the error budget.

Publisher URL: http://arxiv.org/abs/1707.00207

DOI: arXiv:1707.00207v3

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