The MeSsI (Merging Systems Identification) Algorithm & Catalogue

DE LOS RIOS, MARTÍN; DOMÍNGUEZ, R. MARIANO J; PAZ, DANTE; MERCHÁN., MANUEL

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

WILEY-BLACKWELL PUBLISHING, INC

Lugar: Londres; Año: 2016 vol. 458 p. 226 - 232

0035-8711

Merging galaxy systems provide observational evidence of the existence of dark matter and constraints on its properties. Therefore, statisticaly uniform samples of merging systems would be a powerful tool for several studies. In this work 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 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 having experienced a major merger. Next we apply a mixture of Gaussian technique 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 precisely recovered properties. We apply our techniques to 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 their measured properties and discuss future analysis on current and forthcoming samples.