Constraints on fNL from Wilkinson Microwave Anisotropy Probe 7-year data using a neural network classifier

TL;DR

This paper introduces a neural network classifier to measure non-Gaussianity in CMB maps, achieving results comparable to traditional methods but with improved speed and no need for covariance matrix inversion.

Contribution

The paper presents a novel neural network approach for estimating the fNL parameter in CMB data, avoiding covariance matrix inversion and enhancing computational efficiency.

Findings

Neural network classifier yields fNL estimates with standard deviations close to classical methods.

The method is faster and avoids covariance matrix regularization.

Results are consistent across different wavelet transforms.

Abstract

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.