Fortifying the characterization of binary mergers in LIGO data

Tyson B. Littenberg, Michael Coughlin, Benjamin Farr, Will M. Farr

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

The study of compact binary inspirals and mergers with gravitational wave observatories amounts to optimizing a theoretical description of the data to best reproduce the true detector output. While most of the research effort in gravitational wave data modeling focuses on the gravitational waveforms themselves, here we will begin to improve our model of the instrument noise by introducing parameters that allow us to determine the background instrumental power spectrum while simultaneously characterizing the astrophysical signal. We use data from the fifth LIGO science run and simulated gravitational wave signals to demonstrate how the introduction of noise parameters results in the resilience of the signal characterization to variations in an initial estimation of the noise power spectral density. We find substantial improvement in the consistency of Bayes factor calculations when we are able to marginalize over uncertainty in the instrument noise level.

Original languageEnglish (US)
Article number084044
JournalPhysical Review D - Particles, Fields, Gravitation and Cosmology
Volume88
Issue number8
DOIs
StatePublished - Oct 25 2013
Externally publishedYes

Fingerprint Dive into the research topics of 'Fortifying the characterization of binary mergers in LIGO data'. Together they form a unique fingerprint.

Cite this