Data augmentation for galaxy density map reconstruction

TL;DR

This paper introduces a two-step data augmentation method to accurately reconstruct galaxy density maps from incomplete and noisy survey data, preserving statistical properties and inferring missing regions effectively.

Contribution

A novel two-step data augmentation approach for reconstructing galaxy density maps from incomplete, noisy survey data, improving inference of missing regions and statistical accuracy.

Findings

Missing areas are effectively inferred.

Statistical properties of maps are well preserved.

Method outperforms traditional reconstruction techniques.

Abstract

The matter density is an important knowledge for today cosmology as many phenomena are linked to matter fluctuations. However, this density is not directly available, but estimated through lensing maps or galaxy surveys. In this article, we focus on galaxy surveys which are incomplete and noisy observations of the galaxy density. Incomplete, as part of the sky is unobserved or unreliable. Noisy as they are count maps degraded by Poisson noise. Using a data augmentation method, we propose a two-step method for recovering the density map, one step for inferring missing data and one for estimating of the density. The results show that the missing areas are efficiently inferred and the statistical properties of the maps are very well preserved.