Journal Article

Constraints on <i>f</i><sub>NL</sub> from <i>Wilkinson Microwave Anisotropy Probe</i> 7-year data using a neural network classifier

B. Casaponsa, M. Bridges, A. Curto, R. B. Barreiro, M. P. Hobson and E. Martínez-González

in Monthly Notices of the Royal Astronomical Society

Published on behalf of The Royal Astronomical Society

Volume 416, issue 1, pages 457-464
Published in print September 2011 | ISSN: 0035-8711
Published online August 2011 | e-ISSN: 1365-2966 | DOI:
Constraints on fNL from Wilkinson Microwave Anisotropy Probe 7-year data using a neural network classifier

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We 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.

Keywords: methods: data analysis; cosmic background radiation

Journal Article.  5509 words.  Illustrated.

Subjects: Astronomy and Astrophysics

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