ROGER: Reconstructing Orbits of Galaxies in ExtremeRegions using machine learning techniques
Galaxies in the Universe show a wide variety of properties as a result of the action of both, internal and environmental processes. Clusters of galaxies constitute the most extreme environments in the Universe for galaxy evolution. Galaxies in clusters exhibit different properties compared to galaxies that reside in the field, or in less massive systems.
On the other hand, machine learning techniques (machine learning) represent a new way of analyzing big data-sets in an agnostic and homogeneous way. Taking into account the amount of data generated by current and future surveys and simulations, the data-driven techniques will become a fundamental tool for their analysis.

Here we present ROGER (Reconstructing Orbits of Galaxies in Extreme Regions), a machine learning technique that relates the two-dimensional PPSD position of galaxies to their 3D orbits. The code retrieves the probability for each galaxy to belong to each class using only its position on the projected phase-spacei.e, distance to the cluster center (normalized to R200) and relative LOS velocity to the cluster center (normalized to the velocity dispersion).
This code was trained and calibrated using a synthetic catalog of clusters and galaxies constructed using the semi-analytic model of galaxy formation and evolution SAG on the Multidark MDPL2 cosmological simulation.
The code is completely free and public. Can be used as an R package (for more details see https://github.com/Martindelosrios/ROGERor through its online version at https://mdelosrios.shinyapps.io/roger_shiny/.
This project was developed by Martín de los Rios, Julián Martínez, Valeria Coenda, Hernán Muriel, Andrés Ruiz, Cristian Vega and Sofia Cora and was published in the international journal MNRAS.
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