Detection and Analysis of Galaxy Clusters Via a Hierarchical Algorithmic Approach




astronomy, galaxies, galaxy clustering, machine learning, galaxy clusters, OPTICS, clustering, OPTICS algorithm, clustering algorithm, algorithmic approach, galaxy morphology


This paper is a discussion of our analysis of galaxy clustering using an algorithmic approach. Our algorithmic galaxy clustering analysis and galaxy morphology analysis produced promising results in identifying galaxy clusters at different scales, and we used these clusters to draw correlations between cluster membership and galaxy properties such as size and color. We also compare our work in algorithmic galaxy clustering to existing work using machine learning, showing where our results are consistent with previous work, and where they differ from previous work. Overall, we found our research to be insightful into how algorithms perform when finding clusters of galaxies, and we find many possible follow up questions to explore in the future.


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References or Bibliography

Abell, G. O. (1958). The Distribution of Rich Clusters of Galaxies. The Astrophysical Journal Supplement Series, 3, 211.

Allen, S. W., Evrard, A. E., & Mantz, A. B. (2011). Cosmological Parameters from Observations of Galaxy Clusters. Annual Review of Astronomy and Astrophysics, 49(1), 409–470.

Santiago-Bautista, I., Caretta, C. A., Bravo-Alfaro, H., Pointecouteau, E., & Andernach, H. (2020). Identification of filamentary structures in the environment of superclusters of galaxies in the Local Universe. Astronomy & Astrophysics, 637, A31.

SDSS. (n.d.).



How to Cite

Liu, S., Eswaran, P., & Mitra, S. (2021). Detection and Analysis of Galaxy Clusters Via a Hierarchical Algorithmic Approach. Journal of Student Research, 10(3).



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