Constraints onfNL from Wilkinson Microwave Anisotropy Probe7-year data using a neural network classifier

Abstract

We present a multiclass neural network (NN) classifier as a method to measure non-Gaussianity, characterized by the local non-linear coupling parameterfNL, in maps of the cosmic microwave background (CMB) radiation. The classifier is trained on simulated non-Gaussian CMB maps with a range of knownfNL 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 standardχ2minimization. The standard deviations of thefNL estimates forWilkinson Microwave Anisotropy Probe1 like simulations wereσ= 22and 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. © 2011 The Authors Monthly Notices of the Royal Astronomical Society © 2011 RAS.