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  Data series subtraction with unknown and unmodeled background noise

Vitale, S., Congedo, G., Dolesi, R., Ferroni, V., Hueller, M., Vetrugno, D., et al. (2014). Data series subtraction with unknown and unmodeled background noise. Physical Review D, 90: 042003. doi:10.1103/PhysRevD.90.042003.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0024-6961-1 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0024-6962-0
Genre: Journal Article

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 Creators:
Vitale, Stefano, Author
Congedo, Giuseppe, Author
Dolesi, Rita, Author
Ferroni, Valerio, Author
Hueller, Mauro, Author
Vetrugno, Daniele, Author
Weber, William Joseph, Author
Audley, H.1, Author              
Danzmann, K.1, Author              
Diepholz, I.1, Author              
Hewitson, M.2, Author              
Korsakova, Natalia1, Author
Ferraioli, Luigi, Author
Gibert, Ferran, Author
Karnesis, Nikolaos, Author
Nofrarias, Miquel, Author
Inchauspe, Henri, Author
Plagnol, Eric, Author
Jennrich, Oliver, Author
McNamara, Paul W., Author
Armano, Michele, AuthorThorpe, James Ira, AuthorWass, Peter, Author more..
Affiliations:
1Laser Interferometry & Gravitational Wave Astronomy, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, escidoc:24010              
2Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, escidoc:24011              

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Free keywords: General Relativity and Quantum Cosmology, gr-qc, Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM
 Abstract: LISA Pathfinder (LPF), ESA's precursor mission to a gravitational wave observatory, will measure the degree to which two test-masses can be put into free-fall, aiming to demonstrate a residual relative acceleration with a power spectral density (PSD) below 30 fm/s$^2$/Hz$^{1/2}$ around 1 mHz. In LPF data analysis, the measured relative acceleration data series must be fit to other various measured time series data. This fitting is required in different experiments, from system identification of the test mass and satellite dynamics to the subtraction of noise contributions from measured known disturbances. In all cases, the background noise, described by the PSD of the fit residuals, is expected to be coloured, requiring that we perform such fits in the frequency domain. This PSD is unknown {\it a priori}, and a high accuracy estimate of this residual acceleration noise is an essential output of our analysis. In this paper we present a fitting method based on Bayesian parameter estimation with an unknown frequency-dependent background noise. The method uses noise marginalisation in connection with averaged Welch's periodograms to achieve unbiased parameter estimation, together with a consistent, non-parametric estimate of the residual PSD. Additionally, we find that the method is equivalent to some implementations of iteratively re-weighted least-squares fitting. We have tested the method both on simulated data of known PSD, and to analyze differential acceleration from several experiments with the LISA Pathfinder end-to-end mission simulator.

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 Dates: 2014-04-182014-08-042014
 Publication Status: Published in print
 Pages: To appear Phys. Rev. D90 August 2014
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: arXiv: 1404.4792
DOI: 10.1103/PhysRevD.90.042003
URI: http://arxiv.org/abs/1404.4792
 Degree: -

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Title: Physical Review D
  Other : Phys. Rev. D.
Source Genre: Journal
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Publ. Info: Lancaster, Pa. : American Physical Society
Pages: - Volume / Issue: 90 Sequence Number: 042003 Start / End Page: - Identifier: ISSN: 0556-2821
CoNE: http://pubman.mpdl.mpg.de/cone/journals/resource/111088197762258