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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| Published in | IOP conference series. Earth and environmental science Vol. 252; no. 4; pp. 42033 - 42037 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Bristol
IOP Publishing
09.07.2019
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1755-1307 1755-1315 1755-1315 |
| DOI | 10.1088/1755-1315/252/4/042033 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Zhang, Mingrui |
| Author_xml | – sequence: 1 givenname: Mingrui surname: Zhang fullname: Zhang, Mingrui email: 541240857@qq.com organization: Department of Astronomy, Beijing Normal University (Beijing) , China |
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| Cites_doi | 10.1142/S021988781460010X 10.1007/0-306-48096-4 10.1111/j.1365-2966.2012.20675.x |
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| Copyright | Published under licence by IOP Publishing Ltd 2019. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| DOI | 10.1088/1755-1315/252/4/042033 |
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| SubjectTerms | Algorithms Classification Clustering Codec Data points Galactic clusters Galactic evolution Galaxy distribution Mathematical models Stars & galaxies |
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| Title | Use Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm to Identify Galaxy Cluster Members |
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