The XMM Cluster Survey: Exploring scaling relations and completeness of the Dark Energy Survey Year 3 redMaPPer cluster catalogue

Abstract

We cross-match and compare characteristics of galaxy clusters identified in observations from two sky surveys using two completely different techniques. One sample is optically selected from the analysis of three years of Dark Energy Survey observations using the redMaPPer cluster detection algorithm. The second is X-ray selected from XMM observations analysed by the XMM Cluster Survey. The samples comprise a total area of 57.4 deg${}^2$, bounded by the area of 4 contiguous XMM survey regions that overlap the DES footprint. We find that the X-ray selected sample is fully matched with entries in the redMaPPer catalogue, above $\lambda >$20 and within 0.1$<z<$0.9. Conversely, only 38\% of the redMaPPer catalogue is matched to an X-ray extended source. Next, using 120 optically clusters and 184 X-ray selected clusters, we investigate the form of the X-ray luminosity-temperature ($L_X-T_X$), luminosity-richness ($L_X-\lambda$) and temperature-richness ($T_X-\lambda$) scaling relations. We find that the fitted forms of the $L_X-T_X$ relations are consistent between the two selection methods and also with other studies in the literature. However, we find tentative evidence for a steepening of the slope of the relation for low richness systems in the X-ray selected sample. When considering the scaling of richness with X-ray properties, we again find consistency in the relations (i.e., $L_X-\lambda$ and $T_X-\lambda$) between the optical and X-ray selected samples. This is contrary to previous similar works that find a significant increase in the scatter of the luminosity scaling relation for X-ray selected samples compared to optically selected samples.

Accepted for publication to MNRAS

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Cited by in corpus (2)

• YOLO-CL cluster detection in the Rubin/LSST DC2 simulation
• Dark Energy Survey Year 3 Results: Mis-centering calibration and X-ray-richness scaling relations in redMaPPer clusters