Using Minkowski functionals in Machine Learning research

<p>The study of the angular power spectrum of Cosmic Microwave Background (CMB) anisotropies, both in intensity and in polarisation, has led to the tightest constraints on cosmological parameters. However, this statistical quantity is not sensitive to any deviation from Gaussianity and statistical isotropy in the CMB data. Minkowski Functionals (MFs) have been adopted as one of the most powerful statistical tools to study such deviations, since they characterise the topology and geometry of the field of interest. In this paper, we extend the application of MFs to CMB polarisation data by introducing a new formalism, where we lift the spin 2 polarisation field to a scalar function in a higher-dimensional manifold: the group of rotations of the sphere, SO(3). Such a function is defined as f=Qcos(2&psi;)&minus;Usin(2&psi;). We analytically obtain the expected values for the MFs of f in the case of Gaussian isotropic polarisation maps. Furthermore, we present a new pipeline which estimates these MFs from input HEALPix polarisation maps. We apply it to CMB simulations in order to validate the theoretical results and the methodology. The pipeline is to be included in the publicly available Python package Pynkowski available at</p> <p><a href="https://medium.com/@monocosmo77/using-minkowski-functionals-in-machine-learning-research-part2-837bffb7f6ed">Website</a></p>