MeSsI low-redshift merging cluster sample

Therefore, statistically uniform samples of merging systems would be a powerful tool for several studies. In this paper, we present a new methodology for the identification of merging systems and the results of its application to galaxy redshift surveys.

We use as a starting point a mock catalogue of galaxy systems, identified using friends-of-friends algorithms, that have experienced a major merger, as indicated by its merger tree. By applying machine learning techniques in this training sample, and using several features computed from the observable properties of galaxy members, it is possible to select galaxy groups that have a high probability of having experienced a major merger.