Published November 15, 2022 | Published
Journal Article Open

Concurrent estimation of noise and compact-binary signal parameters in gravitational-wave data

Abstract

Gravitational-wave parameter estimation for compact binary signals typically relies on sequential estimation of the properties of the detector Gaussian noise and of the binary parameters. This procedure assumes that the noise variance, expressed through its power spectral density, is perfectly known in advance. We assess the impact of this approximation on the estimated parameters by means of an analysis that simultaneously estimates the noise and compact binary parameters, thus allowing us to marginalize over uncertainty in the noise properties. We compare the traditional sequential estimation method and the new full marginalization method using events from the GWTC-3 catalog. We find that the recovered signals and inferred parameters agree to within their statistical measurement uncertainty. At current detector sensitivities, uncertainty about the noise power spectral density is a subdominant effect compared to other sources of uncertainty.

Additional Information

© 2022 American Physical Society. This work is supported by National Science Foundation Grant No. PHY-1852081 as part of the LIGO Caltech REU Program. This research has made use of data, software and/or web tools obtained from the Gravitational Wave Open Science Center [42], a service of LIGO Laboratory, the LIGO Scientific Collaboration and the Virgo Collaboration. Virgo is funded by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale della Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by Polish and Hungarian institutes. This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation. The authors are grateful for computational resources provided by the LIGO Laboratory and supported by NSF Grants No. PHY-0757058 and No. PHY-0823459. S. H. and K. C. were supported by NSF Grant No. PHY-2110111. Software: gwpy [43], matplotlib [44], corner [45].

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Created:
August 20, 2023
Modified:
October 20, 2023