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

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.