Dependencies for LensGW

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Gravitational Wave Classification

This project focuses on the identification of lensed and unlensed gravitational waves, aiming to extract comprehensive source information. Developed in collaboration with a PhD researcher from the University of Glasgow, the project explores cutting-edge techniques in astrophysics and machine learning to classify gravitational waveforms. Despite hardware limitations, the model achieved 98% accuracy, providing valuable insights into source characteristics.

The project is ongoing, with plans to enhance the accuracy further and integrate additional features.

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Overview

🎯 Goals

1. Classify gravitational waves into lensed and unlensed categories.
2. Generate gravitational waveforms using the PyCBC Python library.
3. Extract and forecast source characteristics from classified waves.

📊 Current Achievements

• 98% classification accuracy using a custom machine learning model.
• Successfully generated gravitational waveforms to simulate real-world conditions.
• Developed a robust methodology for feature extraction and source prediction.

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Features

🌀 Gravitational Waveform Generation

• Leveraged the PyCBC library to generate realistic gravitational waveforms.
• Simulated lensed and unlensed waves for model training and testing.

🤖 Machine Learning Model

• Designed a machine learning model to classify waveforms with high accuracy.
• Employed innovative techniques to overcome hardware limitations.

🔍 Source Analysis

• Forecasted source characteristics based on classified waveforms.
• Provided insights into the origin and nature of gravitational waves.

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Tech Stack

Technology	Description
Python	Core programming language for development.
PyCBC	Used for generating gravitational waveforms.
Scikit-Learn	Machine learning model development and evaluation.
NumPy/Pandas	Data manipulation and analysis.
Matplotlib	Visualization of waveforms and model performance.

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Installation

Follow these steps to set up the project locally:

1. Clone the repository:

   git clone https://github.com/Nancyjikadra/lensGW.git  
   cd lensGW  

2. Create a virtual environment (optional):

   python -m venv venv  
   source venv/bin/activate   # On Windows: venv\Scripts\activate  

3. Install dependencies:

   pip install -r requirements.txt  

4. Run the project:

   python main.py  

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Usage

1. Generating Waveforms

• The script uses PyCBC to generate both lensed and unlensed gravitational waveforms.
• Configuration can be adjusted in the waveform_generator.py file.

2. Training the Model

• The training script uses the generated waveforms to build the classification model.
• Run the training process with:

  python train_model.py  

3. Testing and Evaluation

• Test the model's accuracy using real-world or simulated data.
• Results, including classification metrics and confusion matrices, will be saved in the results/ directory.

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Results

• Accuracy: 98%
• Recall: High recall ensures most gravitational waves are correctly classified.
• Insights: Predicted source characteristics from the classified waveforms.

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Contributions

Contributions are welcome! To contribute:

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

   git checkout -b feature/your-feature-name  

3. Commit your changes.

   git commit -m "Add your message here"  

4. Push your branch.

   git push origin feature/your-feature-name  

5. Open a pull request on GitHub.

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License

This project is licensed under the MIT License.