Incorporation of model accuracy in gravitational wave Bayesian inference
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...
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Published in | Nature astronomy Vol. 9; no. 8; pp. 1256 - 1267 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
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London
Nature Publishing Group UK
01.08.2025
Nature Publishing Group |
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ISSN | 2397-3366 2397-3366 |
DOI | 10.1038/s41550-025-02579-7 |
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Abstract | 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.
The inclusion of model mismatch information in gravitational wave parameter estimation improves on current model-averaging methods, allowing higher-accuracy inferences about the properties of merging black holes. |
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AbstractList | 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.The inclusion of model mismatch information in gravitational wave parameter estimation improves on current model-averaging methods, allowing higher-accuracy inferences about the properties of merging black holes. 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. The inclusion of model mismatch information in gravitational wave parameter estimation improves on current model-averaging methods, allowing higher-accuracy inferences about the properties of merging black holes. 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. |
Author | Thompson, Jonathan E. Hoy, Charlie Akçay, Sarp Mac Uilliam, Jake |
Author_xml | – sequence: 1 givenname: Charlie orcidid: 0000-0002-8843-6719 surname: Hoy fullname: Hoy, Charlie email: charlie.hoy@port.ac.uk organization: Institute of Cosmology and Gravitation, University of Portsmouth – sequence: 2 givenname: Sarp orcidid: 0000-0003-2216-421X surname: Akçay fullname: Akçay, Sarp organization: School of Mathematics & Statistics, University College Dublin – sequence: 3 givenname: Jake orcidid: 0009-0003-5172-2126 surname: Mac Uilliam fullname: Mac Uilliam, Jake organization: School of Mathematics & Statistics, University College Dublin – sequence: 4 givenname: Jonathan E. orcidid: 0000-0002-0419-5517 surname: Thompson fullname: Thompson, Jonathan E. organization: Theoretical Astrophysics Group, California Institute of Technology, Mathematical Sciences & STAG Research Centre, University of Southampton |
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Title | Incorporation of model accuracy in gravitational wave Bayesian inference |
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