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Machine learning for gravitational-wave detection: surrogate Wiener filtering for the prediction and optimized cancellation of Newtonian noise at Virgo

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cris.lastimport.scopus2024-02-12T20:44:29Z
dc.abstract.enThe cancellation of noise from terrestrial gravity fluctuations, also known as Newtonian noise (NN), in gravitational-wave detectors is a formidable challenge. Gravity fluctuations result from density perturbations associated with environmental fields, e.g., seismic and acoustic fields, which are characterized by complex spatial correlations. Measurements of these fields necessarily provide incomplete information, and the question is how to make optimal use of available information for the design of a noise-cancellation system. In this paper, we present a machine-learning approach to calculate a surrogate model of a Wiener filter. The model is used to calculate optimal configurations of seismometer arrays for a varying number of sensors, which is the missing keystone for the design of NN cancellation systems. The optimization results indicate that efficient noise cancellation can be achieved even for complex seismic fields with relatively few seismometers provided that they are deployed in optimal configurations. In the form presented here, the optimization method can be applied to all current and future gravitational-wave detectors located at the surface and with minor modifications also to future underground detectors.
dc.affiliationUniwersytet Warszawski
dc.contributor.authorBadaracco, F.
dc.contributor.authorHarms, J.
dc.contributor.authorBertolini, A.
dc.contributor.authorBulik, Tomasz
dc.contributor.authorFiori, I.
dc.contributor.authorIdźkowski, Bartosz
dc.contributor.authorKutynia, Adam
dc.contributor.authorNikliborc, K.
dc.contributor.authorPaoletti, F.
dc.contributor.authorPaoli, A.
dc.contributor.authorRei, L.
dc.contributor.authorSuchiński, Maciej
dc.date.accessioned2024-01-25T05:30:15Z
dc.date.available2024-01-25T05:30:15Z
dc.date.copyright2020-09-10
dc.date.issued2020
dc.description.accesstimeAT_PUBLICATION
dc.description.financePublikacja bezkosztowa
dc.description.number19
dc.description.versionFINAL_PUBLISHED
dc.description.volume37
dc.identifier.doi10.1088/1361-6382/ABAB64
dc.identifier.issn0264-9381
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/111633
dc.identifier.weblinkhttp://adsabs.harvard.edu/abs/2020CQGra..37s5016B
dc.languageeng
dc.pbn.affiliationastronomy
dc.relation.ispartofClassical and Quantum Gravity
dc.relation.pages195016
dc.rightsCC-BY
dc.sciencecloudnosend
dc.titleMachine learning for gravitational-wave detection: surrogate Wiener filtering for the prediction and optimized cancellation of Newtonian noise at Virgo
dc.typeJournalArticle
dspace.entity.typePublication