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<p>Inferring the properties of colliding black holes from gravitational wave observations is subject to systematic errors arising from modelling uncertainties. Although the accuracy of each model can be calculated through comparison to theoretical expectations from general relativity, Bayesian analyses are yet to incorporate this information. As such, a mixture model is typically used where results obtained with different gravitational wave models are combined with either equal weight or based on their relative Bayesian evidence. In this work we present a new method for incorporating the accuracy of several models into gravitational wave Bayesian analyses. By analysing simulated gravitational wave signals in zero noise, we show that our technique uses 30% less computational resources and more faithfully recovers the true parameters than existing techniques. We further apply our method to a real gravitational wave signal and, when assuming the binary black hole hypothesis, demonstrated that the source of GW191109_010717 has unequal component masses, with a 69% probability for the primary being above the maximum black hole mass from stellar collapse. We envisage that this method will become an essential tool for ground-based gravitational wave astronomy.</p>

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Incorporation of model accuracy in gravitational wave Bayesian inference

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月15日

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  • Charlie Hoy,
  • Sarp Akçay,
  • Jake Mac Uilliam,
  • Jonathan E. Thompson

摘要

Inferring the properties of colliding black holes from gravitational wave observations is subject to systematic errors arising from modelling uncertainties. Although the accuracy of each model can be calculated through comparison to theoretical expectations from general relativity, Bayesian analyses are yet to incorporate this information. As such, a mixture model is typically used where results obtained with different gravitational wave models are combined with either equal weight or based on their relative Bayesian evidence. In this work we present a new method for incorporating the accuracy of several models into gravitational wave Bayesian analyses. By analysing simulated gravitational wave signals in zero noise, we show that our technique uses 30% less computational resources and more faithfully recovers the true parameters than existing techniques. We further apply our method to a real gravitational wave signal and, when assuming the binary black hole hypothesis, demonstrated that the source of GW191109_010717 has unequal component masses, with a 69% probability for the primary being above the maximum black hole mass from stellar collapse. We envisage that this method will become an essential tool for ground-based gravitational wave astronomy.