🌌 BSc Astrophysics Projects

Welcome to the BSc Astrophysics Projects repository! Here, you will find all the astrophysics projects I completed during my final year. My main focus was on optimizing orbital transfers using gradient descent and evaluating uncertainties with the Markov Chain Monte Carlo method.

🚀 Table of Contents

• Introduction
• Project Overview
• Technologies Used
• Installation
• Usage
• Contributing
• License
• Contact
• Releases

📚 Introduction

Astrophysics combines physics and astronomy to understand celestial phenomena. In my final year, I delved into various projects that applied mathematical modeling and statistical methods to real-world problems in space science. This repository serves as a comprehensive collection of my work, showcasing the intersection of theory and practical application.

🌠 Project Overview

The projects in this repository include:

1. Orbital Transfer Optimization: This project employs gradient descent techniques to find the most efficient paths for spacecraft maneuvers. By minimizing fuel consumption and time, we can enhance mission success rates.

2. Uncertainty Evaluation: Using the Markov Chain Monte Carlo method, this project assesses uncertainties in orbital parameters. This approach allows for better risk management in space missions.

3. Mathematical Modeling: Various mathematical models are developed to simulate astrophysical phenomena. These models help in predicting outcomes and understanding complex systems.

4. Data Visualization: Visual representations of data are crucial in astrophysics. This project focuses on creating animations and plots that illustrate findings effectively.

5. Unit Testing: Ensuring the reliability of code is vital. This section covers unit testing practices used throughout the projects.

🛠️ Technologies Used

The following technologies and methodologies are employed in this repository:

• Animation: For visualizing data and processes.
• Astronomy: The foundational science behind the projects.
• Astrophysics: The application of physics to astronomical phenomena.
• Bayesian Inference: A statistical method for uncertainty evaluation.
• Calculus: Essential for modeling and optimization.
• Gradient Descent: A key optimization technique used in various projects.
• LaTeX: For documentation and presentation of mathematical equations.
• Markov Chain Monte Carlo: A method for sampling from probability distributions.
• Mathematical Modeling: Creating models to simulate real-world scenarios.
• Mathematics: The backbone of all computations and analyses.
• Package Development: Building reusable components for the projects.
• Physics: The fundamental science that underpins astrophysics.
• Spacecraft Dynamics and Control: Understanding the motion of spacecraft.
• Statistics: For analyzing data and drawing conclusions.
• Unit Testing: Ensuring code quality and reliability.
• Visualization: Creating visual representations of data and results.
• VSCode: The development environment used for coding.

📥 Installation

To get started with the projects, follow these steps:

1. Clone the Repository:

   git clone https://github.com/delemmaao/BSc_Astrophysics_Projects.git

2. Navigate to the Project Directory:

   cd BSc_Astrophysics_Projects

3. Install Dependencies: Depending on the specific projects you wish to run, you may need to install certain libraries. Check the individual project folders for a requirements.txt or setup.py file.

⚙️ Usage

Each project contains its own README file with detailed instructions on how to run and use the code. You can find these instructions in their respective directories.

To run the main project focused on orbital transfer optimization, use the following command:

python orbital_transfer.py

For uncertainty evaluation using the Markov Chain Monte Carlo method, run:

python mcmc_evaluation.py

Feel free to explore the code and modify it as needed for your own experiments.

🤝 Contributing

Contributions are welcome! If you have suggestions or improvements, please fork the repository and submit a pull request. You can also open issues for any bugs or enhancements you find.

Steps to Contribute:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature/YourFeature

3. Make your changes and commit them:

   git commit -m "Add some feature"

4. Push to the branch:

   git push origin feature/YourFeature

5. Open a pull request.

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

📞 Contact

For any questions or inquiries, feel free to reach out:

• Email: your.email@example.com
• GitHub: delemmaao

📦 Releases

You can find the latest releases and download files from the Releases section. Each release contains important updates and files that you may need to execute.

Feel free to explore the various projects, and I hope you find them insightful. Happy coding!