Galaxy Clustering and Classification using Machine Learning Algorithms and XAI

This paper presents a novel approach to galaxy clustering and analysis using machine learning (ML), coupled with Explainable Artificial Intelligence (XAI) methods. Traditional astronomical research often relies on manual analysis of galaxy clusters, which can be time-consuming and potentially biased. Our study aims to automate this process using advanced computational methods, focusing on clustering algorithms such as Gaussian Mixture Models (GMM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). We also employ classification algorithms including Random Forests, Decision Trees, and Support Vector Machines (SVM). The application of XAI techniques enhances the interpretability and trustworthiness of our models' outputs. By leveraging data from the Sloan Digital Sky Survey (SDSS), we demonstrate the efficacy of our approach in identifying and analyzing galaxy clusters, potentially revolutionizing the field of astronomical research.