Abstract
In this paper a Neural Network-based approach is presented for the real time noise identification of laser interferometric antennas. The 40-meter Caltech laser interferometer output data, used in our experiments, provides a good testbed of algorithms for noise identification (violin resonances in the suspensions, main power harmonics, ring-down noise from servo control systems, electronics noises, glitches and so on) of the interferometric long GW antennas. The algorithms we propose are quite general and robust, taking into account that they do require neither a-priori information on the data, nor precise model, and constitute a powerful tool for data analysis.
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Acernese, F., Milano, L., Barone, F., Eleuteri, A., Tagliaferri, R. (2002). A Neural Network-based approach to system identification for whitening interferometer spectra. In: Tagliaferri, R., Marinaro, M. (eds) Neural Nets WIRN Vietri-01. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0219-9_16
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DOI: https://doi.org/10.1007/978-1-4471-0219-9_16
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