On line power spectra identification and whitening for the noise in interferometric gravitational wave detectors

Abstract

In this paper we address both to the problem of identifying the noise Power Spectral Density of interferometric detectors by parametric techniques and to the problem of the whitening procedure of the sequence of data. We will concentrate the study on a Power Spectral Density like the one of the Italian-French detector VIRGO and we show that with a reasonable finite number of parameters we succeed in modeling a spectrum like the theoretical one of VIRGO, reproducing all its features. We propose also the use of adaptive techniques to identify and to whiten on line the data of interferometric detectors. We analyze the behavior of the adaptive techniques in the field of stochastic gradient and in the Least Squares ones.

28 pages, 21 figures, uses iopart.cls accepted for pubblication on Classical and Quantum Gravity

References in corpus (6)

• Gravitational wave astronomy
• Gravitational Wave Astronomy: in Anticipation of First Sources to be Detected
• An efficient filter for detecting gravitational wave bursts in interferometric detectors
• Data conditioning for gravitational wave detectors: A Kalman filter for regressing suspension violin mode
• Adaptive filtering techniques for gravitational wave interferometric data: Removing long-term sinusoidal disturbances and oscillatory transients
• About the detection of gravitational wave bursts

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