The MeSsI (Merging Systems Identification) Algorithm & Catalogue

TL;DR

This paper introduces the MeSsI algorithm, a machine learning-based method for identifying merging galaxy systems in surveys, providing reliable samples and detailed properties to advance dark matter research.

Contribution

The paper presents a novel machine learning methodology for identifying and analyzing merging galaxy systems using survey data, improving accuracy and reliability over traditional methods.

Findings

Successfully identified known merging systems in SDSS-DR7, WINGS, and HeCS datasets.

Generated new candidate merging systems for further study.

Provided detailed properties of merging systems with low contamination.

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.