Use Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm to Identify Galaxy Cluster Members

Galaxies are important structures for studying the universe, and clusters are the physical environment of galaxies. Their study is of great significance for understanding the evolution of galaxies and the distribution of matter. Classification of galaxies into clusters is an urgent subject. How do w...

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Bibliographic Details
Published in:IOP conference series. Earth and environmental science Vol. 252; no. 4; pp. 42033 - 42037
Main Author: Zhang, Mingrui
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 09.07.2019
Subjects:
Algorithms
Classification
Clustering
Codec
Data points
Galactic clusters
Galactic evolution
Galaxy distribution
Mathematical models
Stars & galaxies
ISSN:1755-1307, 1755-1315, 1755-1315
Online Access:Get full text
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Summary:Galaxies are important structures for studying the universe, and clusters are the physical environment of galaxies. Their study is of great significance for understanding the evolution of galaxies and the distribution of matter. Classification of galaxies into clusters is an urgent subject. How do we classify some observed galaxy data points as clusters? How to ensure the correctness of classification? Based on the results of CoDECS numerical simulation and combining DBSCAN algorithm, this paper attempts to classify the data and compare and explain the results of the three methods. Then, based on the data of Abell 383 cluster, further comparison and analysis of the three methods were made. This research can be a basis on measuring new stars.
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ISSN:1755-1307
1755-1315
1755-1315
DOI:10.1088/1755-1315/252/4/042033