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Sequential simulation-based inference for extreme mass ratio inspirals

Philippa S. Cole1,2,*, James Alvey3,4, Lorenzo Speri5, Christoph Weniger6, Uddipta Bhardwaj6, Davide Gerosa1,2, and Gianfranco Bertone6

  • *Contact author: philippa.cole@unimib.it
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Phys. Rev. D 113, 063030 – Published 16 March, 2026

DOI: https://doi.org/10.1103/4cd3-wfjr

Abstract

Extreme mass-ratio inspirals pose a difficult challenge in terms of both search and parameter estimation for upcoming space-based gravitational-wave detectors such as LISA. Their signals are long and of complex morphology, meaning they carry a large amount of information about their source, but their waveforms are expensive to compute and they occupy a vast and multimodal parameter space. We explore how sequential simulation-based inference methods, specifically truncated marginal neural ratio estimation, could offer solutions to some of the challenges surrounding extreme-mass-ratio inspiral data analysis. We show that this method can efficiently narrow down the volume of the complex 11-dimensional search parameter space by a factor of 106–107 and provide one-dimensional marginal proposal distributions for nonspinning extreme-mass-ratio inspirals. We discuss the current limitations of this approach and place it in the broader context of a global strategy for future space-based gravitational-wave data analysis.

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Phys. Rev. D 113, 063030– Published 16 March, 2026
  • Received 4 July 2025
  • Accepted 9 January 2026
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