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Dependencies for LensGW

alt text

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.

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.

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.

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.

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

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.

Results

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

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.

License

This project is licensed under the MIT License.