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PreprintarXiv licencearXiv

Constructing likelihoods for a non-Gaussian gravitational-wave background

  • Tore Boybeyi
  • Vuk Mandic
  • Joseph Romano
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Abstract

The stochastic gravitational-wave background from stellar-mass compact binaries is non-Gaussian since it is made up of a finite number of individual mergers. Over the years, several methods have been developed to search for this background beyond the standard cross-correlation statistic. We reformulate the data analysis problem for such astrophysical backgrounds in terms of the characteristic function of the data in multiple detectors, and show that the existing searches arise as different limiting cases. We also show that new searches can be constructed through different approximations of the characteristic function, which gives a concrete way to engineer suitable approximate sufficient statistics. To illustrate this framework in the context of a simple example, we use the formalism proposed here to analyze an exactly solvable toy model.

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Cite

BibTeXRIS
BibTeX
@misc{boybeyi2026constructing,
  title = {{Constructing likelihoods for a non-Gaussian gravitational-wave background}},
  author = {Tore Boybeyi and Vuk Mandic and Joseph Romano},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.38765},
  eprint = {2610.38765},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.38765},
}
RIS
TY  - UNPB
TI  - Constructing likelihoods for a non-Gaussian gravitational-wave background
AU  - Tore Boybeyi
AU  - Vuk Mandic
AU  - Joseph Romano
PY  - 2026
DA  - 2026/10/28
PB  - arXiv
DO  - 10.48550/arxiv.2610.38765
UR  - https://arxiv.org/abs/2610.38765
AB  - The stochastic gravitational-wave background from stellar-mass compact binaries is non-Gaussian since it is made up of a finite number of individual mergers. Over the years, several methods have been developed to search for this background beyond the standard cross-correlation statistic. We reformulate the data analysis problem for such astrophysical backgrounds in terms of the characteristic function of the data in multiple detectors, and show that the existing searches arise as different limiting cases. We also show that new searches can be constructed through different approximations of the characteristic function, which gives a concrete way to engineer suitable approximate sufficient statistics. To illustrate this framework in the context of a simple example, we use the formalism proposed here to analyze an exactly solvable toy model.
ER  -