Stellar Insights: Investigating Stellar Composition and Mass as Indicators of Earth-like Exoplanets

📄 Research Paper

This repository contains the code and data used for this work:
A Graph Driven Approach to Complex Challenges A Case Study on Multiobjective Stellar and Earth Like Exoplanet Clustering

📌 Overview

This repository contains code for analyzing stellar metallicity and mass in relation to Earth-like exoplanets using graph-based methods. It includes:

• Interactive graph construction
• Cluster analysis and statistical tests
• Planetary mass and metallicity analysis

⚙️ Installation and Setup

1️⃣ Prerequisites

Ensure you have the following installed:

• Python (>=3.8)
• GCC/G++ (>=11) for compiling C/C++ dependencies

2️⃣ Clone the repository

git clone https://github.com/Matheus-Emanue123/StellarInsights.git
cd StellarInsights

3️⃣ Install dependencies

Create a virtual environment and install required Python packages:

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`
pip install -r requirements.txt

🚀 Running the Project

To execute the main analysis and graph construction, run:

python generate_graphs_and_analysis.py

To run clustering and statistical tests:

python cluster_analysis_and_statistical_tests.py

To analyze metallicity and mass:

python analyze_planet_metallicity_and_mass.py

📚 Required Libraries

The following Python libraries are used:

• numpy
• pandas
• matplotlib
• seaborn
• networkx
• pyvis
• scipy
• openpyxl

Ensure all dependencies are installed before running the scripts.

🔗 Outputs

• Interactive Graphs (.html files)
• Sub-databases for Gephi (.csv files)
• Statistical Analysis (.txt files)
• Clustered Data Visualizations

Feel free to explore and contribute! 🚀