The MeSsI (Merging Systems Identification) Algorithm & Catalogue

Abstract

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.

Submitted to MNRAS Letters (7 pages, 3 figures, 1 table). Revised version after addressing referee's comments, complete table will be available in the published version

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• Relating the structure of dark matter halos to their assembly and environment
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• Identifying Galaxy Cluster Mergers with Deep Neural Networks using Idealized Compton-y and X-ray maps
• The Blooming Tree Algorithm at Work: Clusters, Filaments and Superclusters in the Field of A2029