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Cosmology from Galaxy Redshift Surveys with PointNet
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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 \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.
Forward citations
Cited by 4 Pith papers
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Mining for Protoclusters at $z\sim4$ from Photometric Datasets with Deep Learning
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Learning Cosmology from Nearest Neighbour Statistics
Nearest-neighbour distance maps, combined with kNN-CDFs in a hybrid neural network, constrain Ωm and σ8 from Quijote halos with R2=0.80 and 0.93, matching or beating point-cloud methods at a fraction of the compute.
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Cosmology with Topological Deep Learning
Topological neural networks using tetrahedra, clusters and hyperedges built from halo catalogs lower inference error on Omega_m by 22% and on sigma_8 by up to 60% versus graph neural networks on Quijote.
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Jointly training a graph neural network with a normalizing flow yields low-dimensional summary statistics from simulated galaxy catalogs that support likelihood-free inference of Omega_m, and can be interpreted via co...
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