BibTeX

@article{2505.16795v1,
Author        = {Philippa S. Cole and James Alvey and Lorenzo Speri and Christoph Weniger and Uddipta Bhardwaj and Davide Gerosa and Gianfranco Bertone},
Title         = {Sequential simulation-based inference for extreme mass ratio inspirals},
Eprint        = {2505.16795v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {gr-qc},
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 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.},
Year          = {2025},
Month         = {May},
Url           = {http://arxiv.org/abs/2505.16795v1},
File          = {2505.16795v1.pdf}
}