ROGER: Reconstructing Orbits of Galaxies in Extreme Regions using machine learning techniques

Martín de los Rios; Héctor Julián Martínez; Valeria Coenda; Hernán Muriel; Andrés Nicolás Ruiz; Cristian Antonio Vega-Martínez; Sofia Alejandra Cora

doi:10.1093/mnras/staa3339

paper

ROGER: Reconstructing Orbits of Galaxies in Extreme Regions using machine learning techniques

arXiv:2010.11959 · doi:10.1093/mnras/staa3339

Abstract

We present the ROGER (Reconstructing Orbits of Galaxies in Extreme Regions) code, which uses three different machine learning techniques to classify galaxies in, and around, clusters, according to their projected phase-space position. We use a sample of 34 massive, $M_{200} > 1 0^{15} h^{- 1} M_{⊙}$ , galaxy clusters in the MultiDark Planck 2 (MDLP2) simulation at redshift zero. We select all galaxies with stellar mass $M_{⋆} \geq 1 0^{8.5} h^{- 1} M_{⊙}$ , as computed by the semi-analytic model of galaxy formation SAG, that are located in, and in the vicinity of, the clusters and classify them according to their orbits. We train ROGER to retrieve the original classification of the galaxies out of their projected phase-space positions. For each galaxy, ROGER gives as output the probability of being a cluster galaxy, a galaxy that has recently fallen into a cluster, a backsplash galaxy, an infalling galaxy, or an interloper. We discuss the performance of the machine learning methods and potential uses of our code. Among the different methods explored, we find the K-Nearest Neighbours algorithm achieves the best performance.

arXiv commentAcceptep for its publication in the MNRAS Journal. Code available at github repository

ROGER: Reconstructing Orbits of Galaxies in Extreme Regions using machine learning techniques

Abstract

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