Systematic bias from waveform modeling for binary black hole populations in next-generation gravitational wave detectors
arxiv(2024)
摘要
Next-generation gravitational wave detectors such as the Einstein Telescope
and Cosmic Explorer will have increased sensitivity and observing volumes,
enabling unprecedented precision in parameter estimation. However, this
enhanced precision could also reveal systematic biases arising from waveform
modeling, which may impact astrophysical inference. We investigate the extent
of these biases over a year-long observing run with 10^5 simulated binary
black hole sources using the linear signal approximation. To establish a
conservative estimate, we sample binaries from a smoothed truncated power-law
population model and compute systematic parameter biases between the
IMRPhenomXAS and IMRPhenomD waveform models. For sources with signal-to-noise
ratios above 100, we estimate statistically significant parameter biases in
∼ 3%-20% of the events, depending on the parameter. We find that the
average mismatch between waveform models required to achieve a bias of ≤
1σ for 99% of detections with signal-to-noise ratios ≥ 100 should
be 𝒪(10^-5), or at least one order of magnitude better than
current levels of waveform accuracy.
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