Sequential simulation-based inference for extreme mass ratio inspirals
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916752446717952 |
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| author | Cole, Philippa S. Alvey, James Speri, Lorenzo Weniger, Christoph Bhardwaj, Uddipta Gerosa, Davide Bertone, Gianfranco |
| author_facet | Cole, Philippa S. Alvey, James Speri, Lorenzo Weniger, Christoph Bhardwaj, Uddipta Gerosa, Davide Bertone, Gianfranco |
| contents | 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 are also difficult to search for and analyse. 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_16795 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sequential simulation-based inference for extreme mass ratio inspirals Cole, Philippa S. Alvey, James Speri, Lorenzo Weniger, Christoph Bhardwaj, Uddipta Gerosa, Davide Bertone, Gianfranco General Relativity and Quantum Cosmology Cosmology and Nongalactic Astrophysics High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics 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 are also difficult to search for and analyse. 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. |
| title | Sequential simulation-based inference for extreme mass ratio inspirals |
| topic | General Relativity and Quantum Cosmology Cosmology and Nongalactic Astrophysics High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2505.16795 |