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  Quantitative Analysis of LISA Pathfinder Test Mass Noise

Ferraioli, L., Hewitson, M., Congedo, G., Nofrarias, M., Hueller, M., Armano, M., et al. (2011). Quantitative Analysis of LISA Pathfinder Test Mass Noise. Physical Review D, 84: 122003. doi:10.1103/PhysRevD.84.122003.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-000F-3B65-D Version Permalink: http://hdl.handle.net/11858/00-001M-0000-000F-3B66-B
Genre: Journal Article

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 Creators:
Ferraioli, Luigi, Author
Hewitson, M.1, Author              
Congedo, Giuseppe, Author
Nofrarias, Miquel, Author
Hueller, Mauro, Author
Armano, Michele, Author
Vitale, Stefano, Author
Affiliations:
1Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, ou_24011              

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Free keywords: General Relativity and Quantum Cosmology, gr-qc, Physics, Data Analysis, Statistics and Probability, physics.data-an
 Abstract: In this paper we discuss two main problems associated with the analysis of the data from LISA Pathfinder (LPF): i) Excess noise detection and ii) Noise parameter identification. The mission is focused on the low frequency region ([0.1; 10] mHz) of the available signal spectrum. In such a region the signal is dominated by the force noise acting on test masses. Noise analysis is expected to deal with a limited amount of non-Gaussian data, since the spectrum statistics will be far from Gaussian and the lowest available frequency is limited by the data length. In this paper we analyze the details of the expected statistics for spectral data and develop two suitable excess noise estimators. One is based on the statistical properties of the integrated spectrum, the other is based on Kolmogorov-Smirnov test. The sensitivity of the estimators is discussed theoretically for independent data, then the algorithms are tested on LPF synthetic data. The test on realistic LPF data allows the effect of spectral data correlations on the efficiency of the different noise excess estimators to be highlighted. It also reveals the versatility of the Kolmogorov-Smirnov approach, which can be adapted to provide reasonable results on correlated data from a modified version of the standard equations for the inversion of the test statistic. Closely related to excess noise detection, the problem of noise parameter identification in non-Gaussian data is approached in two ways. One procedure is based on maximum likelihood estimator and another is based on the Kolmogorov-Smirnov goodness of fit estimator. Both approaches provide unbiased and accurate results for noise parameter estimation and demonstrate superior performance with respect to standard weighted least-squares and Huber's norm.

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 Dates: 2011-11-142011
 Publication Status: Published in print
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 Identifiers: arXiv: 1111.3320
DOI: 10.1103/PhysRevD.84.122003
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Title: Physical Review D
  Other : Phys. Rev. D.
Source Genre: Journal
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Publ. Info: Lancaster, Pa. : Published for the American Physical Society by the American Institute of Physics
Pages: - Volume / Issue: 84 Sequence Number: 122003 Start / End Page: - Identifier: ISSN: 0556-2821
CoNE: https://pure.mpg.de/cone/journals/resource/111088197762258