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Description
Merging galaxy systems provide observational evidence of the existence of dark matter and constraints on its properties. 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. Next, we apply a mixture of Gaussian techniques on galaxy members in order to reconstruct the properties of the haloes involved in such mergers. This methodology provides a highly reliable sample of merging systems with low contamination and precisely recovered properties. We apply our techniques to samples of galaxy systems obtained from the Sloan Digital Sky Survey Data Release 7, the Wide-Field Nearby Galaxy-Cluster Survey (WINGS) and the Hectospec Cluster Survey (HeCS). Our results recover previously known merging systems and provide several new candidates. We present their measured properties and discuss future analysis on current and forthcoming samples.
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