The MeSsI (Merging Systems Identification) Algorithm

PAZ, DANTE; DOMINGUEZ, MARIANO; DE LOS RIOS, MARTÍN.; MERCHAN, MANUEL

San Pablo

Workshop; International Conference on the interconection between Particle Physics and Cosmology.; 2016

ICTP-SAIFR

 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 fromthe 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 insuch 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.