Cosmology from Galaxy Redshift Surveys with PointNet

Sotiris Anagnostidis, Arne Thomsen, Tomasz Kacprzak, Tilman Tröster, Luca Biggio, Thomas Hofmann

Abstract:

In recent years, deep learning approaches have achieved state-of-the-art results in
the analysis of point cloud data. In cosmology, galaxy redshift surveys resemble
such a permutation invariant collection of positions in space. These surveys have
so far mostly been analysed with two-point statistics, such as power spectra and
correlation functions. The usage of these summary statistics is best justified on
large scales, where the density field is linear and Gaussian. However, in light of
the increased precision expected from upcoming surveys, the analysis of – intrinsically non-Gaussian – small angular separations represents an appealing avenue
to better constrain cosmological parameters. In this work, we aim to improve
upon two-point statistics by employing a PointNet-like neural network to regress
the values of the cosmological parameters directly from point cloud data. Our implementation of PointNets can analyse inputs of O(104
) − O(105
) galaxies at a
time, which improves upon earlier work for this application by roughly two orders
of magnitude. Additionally, we demonstrate the ability to analyse galaxy redshift
survey data on the lightcone, as opposed to previously static simulation boxes at a
given fixed redshift.