Constraints on fNL from Wilkinson Microwave Anisotropy Probe 7-year data
  using a neural network classifier
  We present a multi-class neural network (NN) classifier as a method to
measure nonGaussianity, characterised 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 (HW) wavelet and the spherical Mexican hat wavelet (SMHW).
When applied to simulated test maps, the NN classfier produces results in very
good agreement with those obtained using standard chi2 minimization. The
standard deviations of the fNL estimates for WMAPlike simulations were {\sigma}
= 22 and {\sigma} = 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 regularise
the matrix when it is not directly invertible, and is considerably faster.
