  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 \textit{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 $\mathcal{O}(10^4) -
\mathcal{O}(10^5)$ 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.
