Searching for galaxy clusters in the Kilo-Degree Survey

Abstract

Aims. In this paper, we present the tools used to search for galaxy clusters in the Kilo Degree Survey (KiDS), and our first results. Methods. The cluster detection is based on an implementation of the optimal filtering technique that enables us to identify clusters as over-densities in the distribution of galaxies using their positions on the sky, magnitudes, and photometric redshifts. The contamination and completeness of the cluster catalog are derived using mock catalogs based on the data themselves. The optimal signal to noise threshold for the cluster detection is obtained by randomizing the galaxy positions and selecting the value that produces a contamination of less than 20%. Starting from a subset of clusters detected with high significance at low redshifts, we shift them to higher redshifts to estimate the completeness as a function of redshift: the average completeness is ∼ 85%. An estimate of the mass of the clusters is derived using the richness as a proxy. Results. We obtained 1858 candidate clusters with redshift 0 < z c < 0.7 and mass 10 13.5 < M 500 < 10 15 M in an area of 114 sq. degrees (KiDS ESO-DR2). A comparison with publicly available Sloan Digital Sky Survey (SDSS)-based cluster catalogs shows that we match more than 50% of the clusters (77% in the case of the redMaPPer catalog). We also cross-matched our cluster catalog with the Abell clusters, and clusters found by XMM and in the Planck-SZ survey; however, only a small number of them lie inside the KiDS area currently available.

The third data release of the Kilo-Degree Survey and associated data products

“…Achieving a Gaussian PSF is done via construction of a convolution kernel that varies across the image, modelled in terms of shapelets (Refregier 2003). The size of the Gaussian PSF is set so as to preserve the seeing as much as possible without deconvolving (which would amplify the noise).…”

“…-(IMAFLAGS_ISO_band & 01010111) == 0 for each band (artefact masking, following Radovich et al 2017); -SG2DPHOT == 0 (star removal); -MAGERR_GAAP_band >0 for each band (bad photometry removal); -MAG_GAAP_U <25.4 && MAG_GAAP_G <25.6 && MAG_GAAP_R <24.7 && MAG_GAAP_I <24.5.…”

“…Based on this data set, the first scientific results focused on weak lensing studies of galaxies and galaxy A134, page 2 of 26 groups in the Galaxy And Mass Assembly (GAMA, Driver et al 2011) fields (Kuijken et al 2015;Sifón et al 2015;Viola et al 2015;Brouwer et al 2016;van Uitert et al 2016). Furthermore, these data releases were the basis for the first results from a z ∼ 6 quasar search (Venemans et al 2015), a catalogue of photometric redshifts from machine-learning (Cavuoti et al 2015(Cavuoti et al , 2017, preliminary results on super-compact massive galaxies (Tortora et al 2016), and a catalogue of galaxy clusters (Radovich et al 2017).…”

Luminous red galaxies in the Kilo-Degree Survey: selection with broad-band photometry and weak lensing measurements

“…The photometric catalogue is cleaned by removing the artefacts corresponding to any of the following masking flags: readout spike, saturation core, diffraction spike, secondary halo, or bad pixels. Furthermore, only objects for which photometric errors in all bands are provided, are kept in the final photometric catalogue (see de Jong et al (2017) and Radovich et al (2017)). Finally, we require the final sample to not contain point-like objects by applying the cut SG2DPHOT = 0.…”

“…This procedure, known as redMagiC, has been successfully applied to the Sloan Digital Sky Survey and the Dark Energy Survey data (Rozo et al 2016). Obtaining a sample of galaxies with a well-defined selection and precise redshifts over the entire footprint of a given galaxy survey has been proven beneficial for galaxy-galaxy lensing studies (Clampitt et al 2017;Prat et al 2017), galaxy clustering (Elvin-Poole et al 2017), and joint cosmological probes.…”