Galaxy pairs in The Three Hundred simulations II: studying bound ones and identifying them via machine learning
Entity
UAM. Departamento de Física TeóricaPublisher
Published by Oxford University Press on behalf of Royal Astronomical SocietyDate
2023-06-01Citation
10.1093/mnras/stad1061
Monthly Notices of the Royal Astronomical Society 522.1 (2023): 1270-1287
ISSN
0035-8711DOI
10.1093/mnras/stad1061Funded by
THE THREE HUNDRED (https://the300-project.org) collaboration; European Union’s Horizon 2020 Research and Innovation programme under the Marie Sklodowskaw-Curie grant agreement number 734374; the LACEGAL project. ACS, AK, WC, and GY thank the Ministerio de Ciencia e Innovación (MICINN) for financial support under research grant PID2021-122603NB-C21. AK further thanks The Who for Quadrophenia. WC is additionally supported by the STFC AGP Grant ST/V000594/1 and the Atracción de Talento Contract no. 2020-T1/TIC-19882 granted by the Comunidad de Madrid in Spain. He further acknowledges the science research grants from the China Manned Space Project with NO. CMS-CSST-2021-A01 and CMS-CSST-2021-B01Project
Gobierno de España. PID2021-122603NB-C21; info:eu-repo/grantAgreement/EC/H2020//734374Editor's Version
https://doi.org/10.1093/mnras/stad1061Subjects
Methods: numerical; galaxies: clusters: general; galaxies: general; galaxies: interactions; FísicaRights
© 2023 The Author(s)Abstract
Using the data set of The Three Hundred project, i.e. 324 hydrodynamical resimulations of cluster-sized haloes and the regions of radius 15 around them, we study galaxy pairs in high-density environments. By projecting the galaxies' 3D coordinates onto a 2D plane, we apply observational techniques to find galaxy pairs. Based on a previous theoretical study on galaxy groups in the same simulations, we are able to classify the observed pairs into 'true' or 'false', depending on whether they are gravitationally bound or not. We find that the fraction of true pairs (purity) crucially depends on the specific thresholds used to find the pairs, ranging from around 30 to more than 80 per cent in the most restrictive case. Nevertheless, in these very restrictive cases, we see that the completeness of the sample is low, failing to find a significant number of true pairs. Therefore, we train a machine learning algorithm to help us identify these true pairs based on the properties of the galaxies that constitute them. With the aid of the machine learning model trained with a set of properties of all the objects, we show that purity and completeness can be boosted significantly using the default observational thresholds. Furthermore, this machine learning model also reveals the properties that are most important when distinguishing true pairs, mainly the size and mass of the galaxies, their spin parameter, gas content, and shape of their stellar components
Files in this item
Google Scholar:Contreras-Santos, Ana
-
Knebe, Alexander
-
Cui, Weiguang
-
Haggar, Roan
-
Pearce, Frazer
-
Gray, Meghan
-
De Petris, Marco
-
Yepes Alonso, Gustavo
This item appears in the following Collection(s)
Related items
Showing items related by title, author, creator and subject.