Cosmography with Galaxy Clusters

TL;DR

This paper develops and tests a computational pipeline for galaxy cluster detection using deep survey images, aiming to improve constraints on dark energy parameters from future experiments.

Contribution

It introduces the 2DPHOT environment and Voronoi Tessellation algorithm for reliable galaxy cluster identification in wide-field surveys.

Findings

Pipeline achieves high completeness and purity in mock catalogs.

Demonstrates effectiveness on LEGACY Survey deep field images.

Provides insights into the selection function of dark matter halos.

Abstract

In the present work we focus on future experiments using cluster abundance observations to constraint the Dark Energy equation of state parameter, w. To obtain tight constraints from this kind of experiment, a reliable sample of galaxy clusters must be obtained from deep and wide-field images. We therefore present the computational environment (2DPHOT) that allow us to build the galaxy catalog from the images and the Voronoi Tessellation cluster finding algorithm that we use to identify the galaxy clusters on those catalogs. To test our pipeline with data similar in quality to what will be gathered by future wide field surveys, we process images from the Deep fields obtained as part of the LEGACY Survey (four fields of one square degree each, in five bands, with depth up to r'=25). We test our cluster finder by determining the completeness and purity of the finder when applied to mock galaxy catalogs made for the Dark Energy Survey cluster finder comparison project by Risa Wechsler and Michael Busha. This procedure aims to understand the selection function of the underlying dark matter halos.