Detection and Analysis of Galaxy Clusters Via a Hierarchical Algorithmic Approach

Abstract

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.