Systematic bias from waveform modeling for binary black hole populations in next-generation gravitational wave detectors

Veome Kapil, Luca Reali, Roberto Cotesta, Emanuele Berti

Credit: ChatGPT / DALLE

Abstract

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 105superscript10510^{5}10 start_POSTSUPERSCRIPT 5 end_POSTSUPERSCRIPT 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%similar-toabsentpercent3percent20\sim 3\%-20\%∼ 3 % - 20 % of the events, depending on the parameter. We find that the average mismatch between waveform models required to achieve a bias of ≤1⁢σabsent1𝜎\leq 1\sigma≤ 1 italic_σ for 99%percent9999\%99 % of detections with signal-to-noise ratios ≥100absent100\geq 100≥ 100 should be 𝒪⁢(10−5)𝒪superscript105\mathcal{O}(10^{-5})caligraphic_O ( 10 start_POSTSUPERSCRIPT - 5 end_POSTSUPERSCRIPT ), or at least one order of magnitude better than current levels of waveform accuracy.