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arXiv · 1503.07476

Regression of Environmental Noise in LIGO Data

Vaibhav Tiwari·Marco Drago·Valery Frolov·Sergey Klimenko·Guenakh Mitselmakher·Valentin Necula·Giovanni Prodi·Virginia Re·Francesco Salemi·Gabriele Vedovato·Igor Yakushin
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Abstract

We address the problem of noise regression in the output of gravitational-wave (GW) interferometers, using data from the physical environmental monitors (PEM). The objective of the regression analysis is to predict environmental noise in the gravitational-wave channel from the PEM measurements. One of the most promising regression method is based on the construction of Wiener-Kolmogorov filters. Using this method, the seismic noise cancellation from the LIGO GW channel has already been performed. In the presented approach the Wiener-Kolmogorov method has been extended, incorporating banks of Wiener filters in the time-frequency domain, multi-channel analysis and regulation schemes, which greatly enhance the versatility of the regression analysis. Also we presents the first results on regression of the bi-coherent noise in the LIGO data.

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Vaibhav Tiwari, Marco Drago, Valery Frolov, Sergey Klimenko, Guenakh Mitselmakher, Valentin Necula, Giovanni Prodi, Virginia Re, Francesco Salemi, Gabriele Vedovato, Igor Yakushin. 2015-03-25. Regression of Environmental Noise in LIGO Data. https://doi.org/10.1088/0264-9381/32/16/165014

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