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  Gaussian processes for the interpolation and marginalization of waveform error in extreme-mass-ratio-inspiral parameter estimation

Chua, A. J. K., Korsakova, N., Moore, C. J., Gair, J., & Babak, S. (2020). Gaussian processes for the interpolation and marginalization of waveform error in extreme-mass-ratio-inspiral parameter estimation. Physical Review D, 101(4): 044027. doi:10.1103/PhysRevD.101.044027.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0005-A57F-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-C19E-F
Genre: Journal Article

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 Creators:
Chua, Alvin J. K., Author
Korsakova, Natalia, Author
Moore, Christopher J., Author
Gair, Jonathan1, Author              
Babak, Stanislav, Author
Affiliations:
1Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society, ou_1933290              

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Free keywords: Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM, Astrophysics, High Energy Astrophysical Phenomena, astro-ph.HE,General Relativity and Quantum Cosmology, gr-qc
 Abstract: A number of open problems hinder our present ability to extract scientific information from data that will be gathered by the near-future gravitational-wave mission LISA. Many of these relate to the modeling, detection and characterization of signals from binary inspirals with an extreme $(\lesssim10^{-4})$ component-mass ratio. In this paper, we draw attention to the issue of systematic error in parameter estimation due to the use of fast but approximate waveform models; this is found to be relevant for extreme-mass-ratio inspirals even in the case of waveforms with $\gtrsim90\%$ overlap accuracy and moderate ($\gtrsim30$) signal-to-noise ratios. A scheme that uses Gaussian processes to interpolate and marginalize over waveform error is adapted and investigated as a possible precursor solution to this problem. Several new methodological results are obtained, and the viability of the technique is successfully demonstrated on a three-parameter example in the setting of the LISA Data Challenge.

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 Dates: 2019-12-242020
 Publication Status: Published in print
 Pages: 12 pages, 6 figures
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 Rev. Method: -
 Identifiers: arXiv: 1912.11543
URI: http://arxiv.org/abs/1912.11543
DOI: 10.1103/PhysRevD.101.044027
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Title: Physical Review D
Source Genre: Journal
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Pages: - Volume / Issue: 101 (4) Sequence Number: 044027 Start / End Page: - Identifier: -