Cosmology from Galaxy Redshift Surveys with PointNet
Abstract
In recent years, deep learning approaches have achieved stateoftheart 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 twopoint 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 nonGaussian  small angular separations represents an appealing avenue to better constrain cosmological parameters. In this work, we aim to improve upon twopoint 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.
 Publication:

arXiv eprints
 Pub Date:
 November 2022
 DOI:
 10.48550/arXiv.2211.12346
 arXiv:
 arXiv:2211.12346
 Bibcode:
 2022arXiv221112346A
 Keywords:

 Astrophysics  Cosmology and Nongalactic Astrophysics;
 Computer Science  Machine Learning