Sequential simulation-based inference for extreme mass ratio inspirals

Authors:
Top Cited Papers
Open Access
Publisher website
Google Scholar
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 10 6 – 10 7 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.
All Related Versions
Funding Information
  • European Research Council (945155)
  • Fondazione Cariplo (2021-0555)
  • Ministero dell'Università e della Ricerca (2022-Z9X4XS)
  • Ministero dell'Università e della Ricerca (SOE2024-0000125)
  • NextGenerationEU
  • H2020 Marie Skłodowska-Curie Actions (101064542)
  • H2020 Marie Skłodowska-Curie Actions (101149270)
  • Horizon 2020 Framework Programme (864035)
  • Italian Research Center on High-Performance Computing, Big Data, and Quantum Computing

This publication has 107 references indexed in Scilit: