Detector and data characterization at GEO 600

Hewitson, M.

doi:10.1088/0264-9381/24/19/s08

Cited by 13 publications

(16 citation statements)

References 21 publications

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“…More details can be found in ref. Hewitson et al (2009) • Each object keeps track of the operations being applied to it. The object-oriented approach allows the adding of a history step each time a method in the toolbox is applied to a given object.…”

Section: The Ltpda Toolboxmentioning

confidence: 99%

State space modelling and data analysis exercises in LISA Pathfinder

Nofrarias,

Antonucci,

Armano

et al. 2013

Preprint

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LISA Pathfinder is a mission planned by the European Space Agency (ESA) to test the key technologies that will allow the detection of gravitational waves in space. The instrument on-board, the LISA Technology package, will undergo an exhaustive campaign of calibrations and noise characterisation campaigns in order to fully describe the noise model. Data analysis plays an important role in the mission and for that reason the data analysis team has been developing a toolbox which contains all the functionality required during operations. In this contribution we give an overview of recent activities, focusing on the improvements in the modelling of the instrument and in the data analysis campaigns performed both with real and simulated data.

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Section: The Ltpda Toolboxmentioning

confidence: 99%

State space modelling and data analysis exercises in LISA Pathfinder

Nofrarias,

Antonucci,

Armano

et al. 2013

Preprint

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“…An application of the noise generation procedure is presented for a two channel system simulating the LISA Technology Package (LTP) along the principal measurement axis [1][2][3][4][5][13][14][15]. The complete set of algorithms are available as MATLAB tools in the framework of the LTPDA toolbox [14,19] and can be freely downloaded, together with the complete toolbox, at the LTPDA project web page [20].…”

Section: A Case Study Ltp Along X Axis a Response Model And Fitmentioning

confidence: 99%

“…The complete set of algorithms are available as MATLAB tools in the framework of the LTPDA toolbox [14,19] and can be freely downloaded, together with the complete toolbox, at the LTPDA project web page [20].…”

Section: A Case Study Ltp Along X Axis a Response Model And Fitmentioning

confidence: 99%

Calibrating spectral estimation for the LISA Technology Package with multichannel synthetic noise generation

et al. 2010

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The scientific objectives of the Lisa Technology Package (LTP) experiment on board of the LISA Pathfinder mission demand for accurate calibration and validation of the data analysis tools in advance of the mission launch. The level of confidence required in the mission outcomes can be reached only by intensive testing the tools on synthetically generated data. A flexible procedure allowing the generation of cross-correlated stationary noise time series was set-up. Multichannel time series with the desired cross-correlation behavior can be generated once a model for a multichannel cross-spectral matrix is provided. The core of the procedure comprises a noise coloring, multichannel filter designed via a frequency-by-frequency eigen-decomposition of the model crossspectral matrix and a subsequent fit in the Z-domain. The common problem of initial transients in filtered time series is solved with a proper initialization of the filter recursion equations. The noise generator performance was tested in a two-dimensional case study of the closed-loop LTP dynamics along the two principal degrees-of-freedom.

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“…This allows us to shed light on the effects of data correlations on the accuracy of the developed excess noise estimators. The analysis procedures are available as MATLAB tools in the framework of the LTPDA Toolbox [10][11][12]; an object oriented MATLAB Toolbox for advanced data analysis.…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis of LISA pathfinder test-mass noise

Ferraioli¹,

Hewitson²,

Congedo³

et al. 2011

Phys. Rev. D

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LISA Pathfinder (LPF) is a mission aiming to test the critical technology for the forthcoming space-based gravitational wave detectors. The main scientific objective of the LPF mission is to demonstrate test-masses free-falling with residual accelerations below 3 × 10 −14 m s −2 / √ Hz at 1 mHz. Reaching such an ambitious target will require a significant amount of system optimisation and characterisation, which will in turn require accurate and quantitative noise analysis procedures. In this paper we discuss two main problems associated with the analysis of the data from 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. At the same time, the mission duration is limited to 90 days and typical data segments will be 24 hours in length. Considering those constraints, 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. We also discuss the advantages of the Kolmogorov-Smirnov formalism for the estimation of confidence intervals of parameter values in correlated data.

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Detector and data characterization at GEO 600

Cited by 13 publications

References 21 publications

State space modelling and data analysis exercises in LISA Pathfinder

State space modelling and data analysis exercises in LISA Pathfinder

Calibrating spectral estimation for the LISA Technology Package with multichannel synthetic noise generation

Quantitative analysis of LISA pathfinder test-mass noise

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