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Frequentist versus Bayesian analyses: Cross-correlation as an approximate sufficient statistic for LIGO-Virgo stochastic background searches

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Matas,  Andrew
Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

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2012.00907.pdf
(Preprint), 808KB

PhysRevD.103.062003.pdf
(Publisher version), 867KB

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Citation

Matas, A., & Romano, J. D. (2021). Frequentist versus Bayesian analyses: Cross-correlation as an approximate sufficient statistic for LIGO-Virgo stochastic background searches. Physical Review D, 103: 062003. doi:10.1103/PhysRevD.103.062003.


Cite as: https://hdl.handle.net/21.11116/0000-0007-A47E-3
Abstract
Sufficient statistics are combinations of data in terms of which the
likelihood function can be rewritten without loss of information. Depending on
the data volume reduction, the use of sufficient statistics as a preliminary
step in a Bayesian analysis can lead to significant increases in efficiency
when sampling from posterior distributions of model parameters. Here we show
that the frequency integrand of the cross-correlation statistic and its
variance are approximate sufficient statistics for ground-based searches for
stochastic gravitational-wave backgrounds. The sufficient statistics are
approximate because one works in the weak-signal approximation and uses
measured estimates of the auto-correlated power in each detector. Using
analytic and numerical calculations, we prove that LIGO-Virgo's hybrid
frequentist-Bayesian parameter estimation analysis is equivalent to a fully
Bayesian analysis. This work closes a gap in the LIGO-Virgo literature, and
suggests directions for additional searches.