Cosmology from Galaxy Redshift Surveys with PointNet by Sotiris Anagnostidis et al. on Thursday 24 November

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.

arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2211.12346v1