The MeSsI (Merging Systems Identification) Algorithm & Catalogue
  Merging galaxy systems provides observational evidence of the existence of
dark matter and constraints on its properties. Therefore, statistical uniform
samples of merging systems would be a powerful tool for several studies. In
this work we presents a new methodology for merging systems identification and
the results of its application to galaxy redshift surveys. We use as starting
point a mock catalogue of galaxy systems, identified using traditional FoF
algorithms, which experienced a major merger as indicated by its merger tree.
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 with a high probability of have been
experienced a major merger. Next we apply clustering techniques on galaxy
members in order to reconstruct the properties of the haloes involved in such
merger. This methodology provides a highly reliable sample of merging systems
with low contamination and precise recovered properties. We apply our
techniques in samples of galaxy systems obtained from SDSS-DR7, WINGS and HeCS.
Our results recover previously known merging systems and provide several new
candidates. We present its measured properties and discuss future analysis on
current and forthcoming samples.
