Physics Projects Portfolio

Advanced computational physics projects completed for MIT's Data Science for Physics course, showcasing expertise in signal processing, particle physics, and data analysis.

🌐 Live Website

Visit the interactive portfolio: Physics Projects GitHub Pages

📋 Project Overview

This repository contains two major computational physics projects that demonstrate advanced data analysis techniques in modern physics research:

1. Gravitational Wave Detection

• Focus: Signal processing and analysis of LIGO data
• Technologies: Python, Signal Processing Algorithms, LIGO Open Science Center Data
• Key Features:
  • Advanced noise reduction techniques
  • Automated gravitational wave signal detection
  • Waveform fitting and analysis
  • Time-partitioned data processing
• Files: Gravitational Wave Detection/project-1.ipynb, Gravitational Wave Detection/project-1.html

2. Weak Mixing Angle Measurement

• Focus: Analysis of W and Z boson production in LHC collisions
• Technologies: Python, ROOT, LHC Data (8 TeV & 13 TeV)
• Key Features:
  • Jet physics and particle identification
  • Background suppression algorithms
  • Statistical analysis of particle collisions
  • Measurement of fundamental Standard Model parameters
• Files: Measuring Sin_theta/project-2_1.ipynb, Measuring Sin_theta/project-2_2.ipynb

🚀 Getting Started

Prerequisites

• Python 3.7+
• Jupyter Notebook
• ROOT (for particle physics analysis)
• Required Python packages:

  pip install numpy matplotlib scipy uproot mplhep lmfit

Running the Projects

1. Clone the repository:

   git clone https://github.com/ten-jampa/physics_projects.git
   cd physics_projects

2. For Gravitational Wave Detection:

   cd "Gravitational Wave Detection"
   jupyter notebook project-1.ipynb

3. For Weak Mixing Angle Measurement:

   cd "Measuring Sin_theta"
   jupyter notebook project-2_1.ipynb
   jupyter notebook project-2_2.ipynb

📊 Key Results

Gravitational Wave Detection

• Successfully implemented automated signal detection algorithms
• Achieved accurate waveform fitting with chi-square minimization
• Demonstrated robust noise reduction and signal extraction
• Validated results against theoretical predictions

Weak Mixing Angle Measurement

• Identified and characterized W boson mass peaks
• Implemented effective background suppression techniques
• Achieved competitive statistical precision in parameter measurement
• Contributed to Standard Model validation

🛠️ Technologies Used

• Programming Languages: Python
• Data Analysis: ROOT, NumPy, SciPy
• Visualization: Matplotlib, mplhep
• Signal Processing: Custom algorithms for GW detection
• Statistical Analysis: lmfit, curve fitting techniques
• Data Sources: LIGO Open Science Center, LHC Data

📁 Repository Structure

physics_projects/
├── index.html                 # Main GitHub Pages site
├── projects/
│   ├── gravitational-waves.html    # GW project page
│   └── weak-mixing-angle.html      # WMA project page
├── Gravitational Wave Detection/
│   ├── project-1.ipynb        # Jupyter notebook
│   └── project-1.html         # HTML export
├── Measuring Sin_theta/
│   ├── project-2_1.ipynb      # Part 1 notebook
│   └── project-2_2.ipynb      # Part 2 notebook
└── README.md                  # This file

🌟 Features

• Interactive Portfolio: Modern, responsive GitHub Pages website
• Comprehensive Documentation: Detailed project descriptions and methodologies
• Educational Content: Clear explanations of physics concepts
• Extensible Design: Easy to add new projects in the future
• Professional Presentation: Clean, modern UI with smooth navigation

🔮 Future Projects

The repository is designed to easily accommodate additional physics projects. New projects can be added by:

1. Creating a new project directory
2. Adding project files (notebooks, data, etc.)
3. Creating a corresponding project page in the projects/ directory
4. Updating the main index.html to include the new project

📚 Academic Context

These projects were completed as part of MIT's Data Science for Physics course, demonstrating:

• Advanced computational physics techniques
• Real-world data analysis skills
• Understanding of modern physics research methods
• Proficiency in scientific computing tools

🤝 Contributing

This is a personal academic portfolio, but suggestions for improvements are welcome. Please feel free to:

• Report issues or bugs
• Suggest enhancements to the website
• Propose additional physics projects

📄 License

This project is for educational and portfolio purposes. The original course materials and data sources have their respective licenses and usage terms.

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This portfolio showcases the intersection of theoretical physics and computational data science, demonstrating practical applications of advanced analytical techniques in modern physics research.