Constraining the parameters of GW150914 & GW170104 with numerical relativity surrogates
arXiv:1808.08004 · doi:10.1103/PhysRevD.99.124005
Abstract
Gravitational-wave detectors have begun to observe coalescences of heavy black holes at a consistent pace for the past few years. Accurate models of gravitational waveforms are essential for unbiased and precise estimation of source parameters. Recently developed surrogate models based on high-accuracy numerical relativity (NR) simulations are ideal for constraining physical parameters of heavy black hole merger events. In this paper, we first demonstrate the viability of these multi-modal surrogates as reliable parameter estimation tools. We show that NR surrogates can extract additional information from GW data that is inaccessible to traditional models, by analyzing a set of synthetic signals with the NR surrogate and other approximants. We also consider the case of two of the earliest binary black holes detected by the LIGO observatories: GW150914 and GW170104. We reanalyze their data with fully-precessing NR-surrogate templates and freely provide the resulting posterior samples as supplemental material. We find that our refined analysis is able to extract information from sub-dominant GW harmonics in data, and therefore better resolve the degeneracy in measuring source luminosity distance and orbital inclination for both events. We estimate the sources of both events to be 20-25% further away than was previously estimated. Our analyses also constrain their orbital orientation more tightly around face-on or face-off configurations than before. Additionally, for GW150914 we constrain the effective inspiral spin more tightly around zero. This work is one of the first to unambiguously extract sub-dominant GW mode information from real events. It is also a first step toward eliminating the approximations used in semi-analytic waveform models from GW parameter estimation. It also motivates that NR surrogates be extended to cover more of the binary black hole parameter space.
arXiv comment20 pages, 14 figures; Updated to published version
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