| Title: |
Sequential simulation-based inference for extreme mass ratio inspirals |
| Authors: |
Cole, Philippa S.; Alvey, James; Speri, Lorenzo; Weniger, Christoph; Bhardwaj, Uddipta; Gerosa, Davide; Bertone, Gianfranco |
| Contributors: |
European Research Council; Fondazione Cariplo; Ministero dell’Università e della Ricerca; NextGenerationEU; H2020 Marie Sk?odowska-Curie Actions; Horizon 2020 Framework Programme; Italian Research Center on High-Performance Computing, Big Data, and Quantum Computing |
| Source: |
Physical Review D ; volume 113, issue 6 ; ISSN 2470-0010 2470-0029 |
| Publisher Information: |
American Physical Society (APS) |
| Publication Year: |
2026 |
| Description: |
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 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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1103/4cd3-wfjr |
| DOI: |
10.1103/4cd3-wfjr/fulltext |
| Availability: |
https://doi.org/10.1103/4cd3-wfjr; https://link.aps.org/article/10.1103/4cd3-wfjr; http://harvest.aps.org/v2/journals/articles/10.1103/4cd3-wfjr/fulltext |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.E0BA210F |
| Database: |
BASE |