2022MNRAS.513.4951R

Numerical simulations have shown that massive dark matter haloes, which today host galaxy clusters, assemble their mass over time alternating periods of quiescent accretion and phases of rapid growth associated with major merger episodes. Observations of such events in clusters can provide insights on the astrophysical processes that characterize the properties of the intra-cluster medium, as well as the gravitational processes that contribute to their assembly. It is therefore of prime interest to devise a fast and reliable way of detecting such perturbed systems. We present a novel approach to identifying and timing major mergers in clusters characterized by large values of halo sparsity. Using halo catalogues from the MultiDark-Planck2 simulation, we show that major merger events disrupt the radial mass distribution of haloes, thus leaving a distinct universal imprint on the evolution of halo sparsity over a period not exceeding two dynamical times. We exploit this feature using numerically calibrated distributions to test whether an observed galaxy cluster with given sparsity measurements has undergone a recent major merger and to eventually estimate when such an event occurred. We implement these statistical tools in a specifically developed public PYTHON library LAMMAS, which we apply to the analysis of Abell 383 and Abell 2345 as test cases. Finding that, for example, Abell 2345 had a major merger about 2.1 ± 0.2 Gyr ago. This work opens the way to detecting and timing major mergers in galaxy clusters solely through measurements of their mass at different radii.