2024/03/29 by Veome Kapil, Luca Reali, Roberto Cotesta +2 · 1 voice · 2 citations
Engineering · Mathematics · Physics and Astronomy · #Astrophysics #Binary black hole #Binary number #Black hole (networking) #Computer science #Computer security #Detector #Geophysics and Sensor Technology #Gravitational wave #Mathematics #Optics #Physics #Pulsars and Gravitational Waves Research #Quantum mechanics #Voltage #Waveform #astro-ph.HE #gr-qc
paper · pdf · doi:10.1103/physrevd.109.104043
arxiv published 2024/03/29 · openalex publication_date 2024/05/13 · arxiv updated 2024/05/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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 105 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 \ensuremath∼3%--20% of the events, depending on the parameter. We find that the average mismatch between waveform models required to achieve a bias of \ensuremath≤1\ensuremathσ for 99% of detections with signal-to-noise ratios \ensuremath≥100 should be O(10^\ensuremath-5), or at least one order of magnitude better than current levels of waveform accuracy.