Normal/independent noise in VIRGO data

F. Acernese, P. Amico, M. Alshourbagy, F. Antonucci, S. Aoudia, S. Avino, D. Babusci, G. Ballardin, F. Barone, L. Barsotti, M. Barsuglia, F. Beauville, S. Bigotta, S. Birindelli, M.A. Bizouard, C. Boccara, F. Bondu, L. Bosi, C. Bradaschia, S. BracciniA. Brillet, V. Brisson, L. Brocco, D. Buskulic, E. Calloni, E. Campagna, F. Cavalier, R. Cavalieri, G. Cella, E. Cesarini, E. Chassande-Mottin, C. Corda, F. Cottone, A-C. Clapson, F. Cleva, J-P. Coulon, E. Cuoco, A. Dari, V. Dattilo, M. Davier, R. de Rosa, L. di Fiore, A. di Virgilio, B. Dujardin, A. Eleuteri, D. Enard, I. Ferrante, F. Fidecaro, I. Fiori, R. Flaminio, J-D. Fournier, O. Francois, S. Frasca, F. Frasconi, A. Freise, L. Gammaitoni, F. Garufi, A. Gennai, A. Giazotto, G. Giordano, L. Giordano, R. Gouaty, D. Grosjean, G. Guidi, S. Hebri, H. Heitmann, P. Hello, L. Holloway, S. Karkar, S. Kreckelbergh, P. la Penna, M. Laval, N. Leroy, N. Letendre, M. Lorenzini, V. Loriette, M. Loupias, G. Losurdo, J-M. Mackowski, E. Majorana, C.N. Man, M. Mantovani, F. Marchesoni, F. Marion, J. Marque, F. Martelli, A. Masserot, M. Mazzoni, L. Milano, C. Moins, J. Moreau, N. Morgado, B. Mours, A. Pai, C. Palomba, F. Paoletti, S. Pardi, A. Pasqualetti, R. Passaquieti, D. Passuello, B. Perniola, F. Piergiovanni, L. Pinard, R. Poggiani, M. Punturo, P. Puppo, K. Qipiani, P. Rapagnani, V. Reita, A. Remillieux, F. Ricci, I. Ricciardi, P. Ruggi, G. Russo, S. Solimeno, A. Spallicci, R. Stanga, R. Taddei, M. Tonelli, A. Toncelli, E. Tournefier, F. Travasso, G. Vajente, D. Verkindt, F. Vetrano, A. Viceré, J-Y. Vinet, H. Vocca, M. Yvert, Z. Zhang

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

The analysis of data taken during the C7 VIRGO commissioning run showed strong deviations from Gaussian noise. In this work, we explore a family of distributions, derived from the hypothesis that heavy tails are an effect of a particular kind of nonstationarity, heterocedasticity (i.e. nonuniform variance), that appear to fit VIRGO noise better than a model based on the assumption of Gaussian noise. To estimate the parameters of the noise process (including the heterogeneous variance) we derived an expectation-maximization algorithm. We show the consequences of non-Gaussianity on the fitting of autoregressive filters and on the derivation of test statistics for matched filter operation. Finally, we apply the new noise model to the fitting of an autoregressive filter for whitening of data. © 2006 IOP Publishing Ltd.
Original languageEnglish
JournalClassical and Quantum Gravity
Volume23
Issue number19
DOIs
Publication statusPublished - 7 Oct 2006
Externally publishedYes

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