How does our choice of observable influence our estimation of the centre of a galaxy cluster? Insights from cosmological simulations

TL;DR

This study investigates how the choice of observable—stars, hot gas, or mass distribution—affects the estimated centre of galaxy clusters, revealing that BCG centres align better with gravitational potential minima than X-ray centres, especially under AGN feedback.

Contribution

It systematically compares different measures of cluster centres across cosmological simulations, emphasizing the impact of observable choice on cluster analysis.

Findings

BCG centres correlate strongly with gravitational potential minima.

AGN feedback increases offsets between X-ray peaks and potential minima.

Choice of observable significantly influences cluster centre identification.

Abstract

Galaxy clusters are an established and powerful test-bed for theories of both galaxy evolution and cosmology. Accurate interpretation of cluster observations often requires robust identification of the location of the centre. Using a statistical sample of clusters drawn from a suite of cosmological simulations in which we have explored a range of galaxy formation models, we investigate how the location of this centre is affected by the choice of observable - stars, hot gas, or the full mass distribution as can be probed by the gravitational potential. We explore several measures of cluster centre: the minimum of the gravitational potential, which would expect to define the centre if the cluster is in dynamical equilibrium; the peak of the density; the centre of BCG; and the peak and centroid of X-ray luminosity. We find that the centre of BCG correlates more strongly with the minimum of the gravitational potential than the X-ray defined centres, while AGN feedback acts to significantly enhance the offset between the peak X-ray luminosity and minimum gravitational potential. These results highlight the importance of centre identification when interpreting clusters observations, in particular when comparing theoretical predictions and observational data.