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dc.contributor.authorCasaponsa, Biuse-
dc.contributor.authorBridges, M.-
dc.contributor.authorCurto, Andrés-
dc.contributor.authorBarreiro, R. Belén-
dc.contributor.authorHobson, M. P.-
dc.contributor.authorMartínez-González, Enrique-
dc.date.accessioned2012-02-20T11:04:10Z-
dc.date.available2012-02-20T11:04:10Z-
dc.date.issued2011-09-
dc.identifier.citationMonthly Notices of the Royal Astronomical Society 41681): 457-464 (2011)es_ES
dc.identifier.issn0035-8711-
dc.identifier.urihttp://hdl.handle.net/10261/45906-
dc.description8 páginas, 6 figuras, 1 tabla.-- El Pdf del artículo es la versión pre-print: arXiv:1105.6116v2es_ES
dc.description.abstractWe present a multiclass neural network (NN) classifier as a method to measure non-Gaussianity, characterized by the local non-linear coupling parameter fNL, in maps of the cosmic microwave background (CMB) radiation. The classifier is trained on simulated non-Gaussian CMB maps with a range of known fNL values by providing it with wavelet coefficients of the maps; we consider both the HEALPix wavelet (HW) and the spherical Mexican hat wavelet (SMHW). When applied to simulated test maps, the NN classifier produces results in very good agreement with those obtained using standard χ2 minimization. The standard deviations of the fNL estimates for Wilkinson Microwave Anisotropy Probe1 like simulations were σ= 22 and 33 for the SMHW and the HW, respectively, which are extremely close to those obtained using classical statistical methods in Curto et al. and Casaponsa et al. Moreover, the NN classifier does not require the inversion of a large covariance matrix, thus avoiding any need to regularize the matrix when it is not directly invertible, and is considerably faster.es_ES
dc.description.sponsorshipWe acknowledge partial financial support from the Spanish Ministerio de Ciencia e Innovación project AYA2010-21766-C03-01 and from the CSIC-the Royal Society joint project with reference 2008GB0012 and the Consolider Ingenio-2010 Programme project CSD2010-00064. BC thanks the Spanish Ministerio de Ciencia e Innovación for a pre-doctoral fellowship.es_ES
dc.language.isoenges_ES
dc.publisherWiley-Blackwelles_ES
dc.publisherRoyal Astronomical Societyes_ES
dc.rightsopenAccesses_ES
dc.subjectMethods: data analysises_ES
dc.subjectCosmic background radiationes_ES
dc.titleConstraints on fNL from Wilkinson Microwave Anisotropy Probe 7-year data using a neural network classifieres_ES
dc.typeartículoes_ES
dc.identifier.doi10.1111/j.1365-2966.2011.19053.x-
dc.description.peerreviewedPeer reviewedes_ES
dc.relation.publisherversionhttp://dx.doi.org/10.1111/j.1365-2966.2011.19053.xes_ES
dc.identifier.e-issn1365-2966-
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