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

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, 200 > 1015ℎ −1 , galaxy c...

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Detalles Bibliográficos
Autores principales: Rios, Martín de los, Martínez, Héctor J., Coenda, Valeria, Muriel, H., Ruiz, Andrés N., Vega Martínez, Cristian A., Cora, Sofía Alejandra
Formato: Articulo Preprint
Lenguaje:Inglés
Publicado: 2020
Materias:
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/125930
Aporte de:
id I19-R120-10915-125930
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Astronómicas
galaxies: clusters: general
galaxy: haloes
galaxies: kinematics and dynamics
methods: numerical
methods: analytical
spellingShingle Ciencias Astronómicas
galaxies: clusters: general
galaxy: haloes
galaxies: kinematics and dynamics
methods: numerical
methods: analytical
Rios, Martín de los
Martínez, Héctor J.
Coenda, Valeria
Muriel, H.
Ruiz, Andrés N.
Vega Martínez, Cristian A.
Cora, Sofía Alejandra
ROGER: Reconstructing Orbits of Galaxies in Extreme Regions using machine learning techniques
topic_facet Ciencias Astronómicas
galaxies: clusters: general
galaxy: haloes
galaxies: kinematics and dynamics
methods: numerical
methods: analytical
description 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, 200 > 1015ℎ −1 , galaxy clusters in the MultiDark Planck 2 (MDLP2) simulation at redshift zero. We select all galaxies with stellar mass ★ > 108.5ℎ −1 , as computed by the semi-analytic model of galaxy formation SAG, that are located in, and in the vicinity of, these clusters and classify them according to their orbits. We train ROGER to retrieve the original classification of the galaxies from 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.
format Articulo
Preprint
author Rios, Martín de los
Martínez, Héctor J.
Coenda, Valeria
Muriel, H.
Ruiz, Andrés N.
Vega Martínez, Cristian A.
Cora, Sofía Alejandra
author_facet Rios, Martín de los
Martínez, Héctor J.
Coenda, Valeria
Muriel, H.
Ruiz, Andrés N.
Vega Martínez, Cristian A.
Cora, Sofía Alejandra
author_sort Rios, Martín de los
title ROGER: Reconstructing Orbits of Galaxies in Extreme Regions using machine learning techniques
title_short ROGER: Reconstructing Orbits of Galaxies in Extreme Regions using machine learning techniques
title_full ROGER: Reconstructing Orbits of Galaxies in Extreme Regions using machine learning techniques
title_fullStr ROGER: Reconstructing Orbits of Galaxies in Extreme Regions using machine learning techniques
title_full_unstemmed ROGER: Reconstructing Orbits of Galaxies in Extreme Regions using machine learning techniques
title_sort roger: reconstructing orbits of galaxies in extreme regions using machine learning techniques
publishDate 2020
url http://sedici.unlp.edu.ar/handle/10915/125930
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