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arXiv · 2505.16795

Sequential simulation-based inference for extreme mass ratio inspirals

Philippa S. Cole·James Alvey·Lorenzo Speri·Christoph Weniger·Uddipta Bhardwaj·Davide Gerosa·Gianfranco Bertone
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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 multi-modal 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 $10^6-10^7$ and provide 1-dimensional marginal proposal distributions for non-spinning 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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Philippa S. Cole, James Alvey, Lorenzo Speri, Christoph Weniger, Uddipta Bhardwaj, Davide Gerosa, Gianfranco Bertone. 2025-05-22. Sequential simulation-based inference for extreme mass ratio inspirals. https://arxiv.org/abs/2505.16795

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