Clustering of red-sequence galaxies in the fourth data release ofthe Kilo-Degree Survey

Abstract

We present a sample of luminous red-sequence galaxies to study the large-scale structure in the fourth data release of the Kilo-Degree Survey. The selected galaxies are defined by a red-sequence template, in the form of a data-driven model of the colour-magnitude relation conditioned on redshift. In this work, the red-sequence template is built using the broad-band optical+near infrared photometry of KiDS-VIKING and the overlapping spectroscopic data sets. The selection process involves estimating the red-sequence redshifts, assessing the purity of the sample, and estimating the underlying redshift distributions of redshift bins. After performing the selection, we mitigate the impact of survey properties on the observed number density of galaxies by assigning photometric weights to the galaxies. We measure the angular two-point correlation function of the red galaxies in four redshift bins, and constrain the large scale bias of our red-sequence sample assuming a fixed $\Lambda$CDM cosmology. We find consistent linear biases for two luminosity-threshold samples (dense and luminous). We find that our constraints are well characterized by the passive evolution model.

submitted to A&A

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• Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning
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• Improving Photometric Galaxy Clustering Constraints With Cross-Bin Correlations