Exploiting Machine Learning and Disequilibrium in Galaxy Clusters to Obtain a Mass Profile

Exploiting Machine Learning and Disequilibrium in Galaxy Clusters to Obtain a Mass Profile

We use 3D k -means clustering to characterize galaxy substructure in the A2146 cluster of galaxies ( z = 0.2343). This method objectively characterizes the cluster’s substructure using projected position and velocity data for 67 galaxies within a 2.305 Mpc circular region centered on the cluster...

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Bibliographic Details
Main Authors: Mark J. Henriksen, Prajwal Panda
Format: Article
Language:English
Published: IOP Publishing 2024-01-01
Series:The Astrophysical Journal Letters
Subjects:
Galaxy clusters
Astroinformatics
Large-scale structure of the universe
Dark matter distribution
Online Access:https://doi.org/10.3847/2041-8213/ad1ede

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