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Leveraging rapid parameter estimates for efficient gravitational-wave Bayesian inference via posterior repartitioning

Metha Prathaban*

Charlie Hoy and Michael J. Williams

  • Kavli Institute for Cosmology, Madingley Road, Cambridge CB3 0HA, United Kingdom and Cavendish Laboratory, J.J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom

  • *Contact author: myp23@cam.ac.uk
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Phys. Rev. D 114, 063034 – Published 17 September, 2026

DOI: https://doi.org/10.1103/pncs-9zd5

This article was published on 17 September, 2026. Please update your links.

Abstract

Gravitational-wave astronomy typically relies on rigorous, computationally expensive Bayesian analyses. Several methods have also been developed to perform rapid, approximate Bayesian inference. We present a novel approach to leverage the results of these low-latency analyses to accelerate the final inference, while ensuring that the Bayesian prior remains independent of the data. By combining the fast constraints from the simple-pe algorithm with the nested sampling acceleration technique of posterior repartitioning, we demonstrate that our method can guide the nested sampler toward the most probable regions of parameter space more efficiently for signal-to-noise ratios (SNR) greater than 20, while mathematically guaranteeing that the final inference is identical to that of a standard, uninformed analysis. We validate the method through an injection study on signals with SNR<150, demonstrating that it produces statistically robust and unbiased results while providing speedups of up to 210%, with a mean speedup of 34% for SNRs>20. Importantly, we show that the performance gain provided by our method scales with SNR, establishing it as a powerful technique to mitigate the cost of analyzing signals from current and future gravitational-wave observatories.

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Phys. Rev. D 114, 063034– Published 17 September, 2026
  • Received 9 February 2026
  • Revised 6 August 2026
  • Accepted 13 August 2026
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