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StellarNet 🌟

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Python PyTorch License arXiv

StellarNet: An AI system probing the possibility that stars may possess primitive forms of information processing. By analyzing complex patterns in stellar emissions using deep learning, we search for signatures of self-organization and structured behavior that transcend random processes.

Overview

This project implements a comprehensive analysis pipeline for investigating potential "consciousness-like" patterns in stellar data using PyTorch and astronomical data from TESS and Kepler missions.

Features

  • 🔬 Real-time analysis of stellar light curves from TESS/Kepler missions
  • 🧠 LSTM-based pattern detection for stellar behavior prediction
  • 📊 Comprehensive entropy and frequency analysis
  • 🔍 Anomaly detection in stellar emissions
  • 📈 Advanced visualization of stellar patterns

Installation

# Clone the repository
git clone https://github.com/Agora-Lab-AI/StellarNet.git
cd StellarNet

# Create and activate a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

# Install dependencies
pip install -r requirements.txt

Quick Start

python main.py

By default, the script analyzes a set of pre-selected variable stars. To analyze specific stars:

python main.py --star_id "TIC 260128333" --mission "TESS"

Requirements

  • Python 3.10+
  • PyTorch
  • lightkurve
  • astropy
  • numpy
  • pandas
  • scipy
  • scikit-learn
  • matplotlib

See requirements.txt for complete list.

Methodology

Our analysis pipeline consists of several key components:

  1. Data Collection: Automated fetching of stellar light curves from TESS/Kepler missions
  2. Preprocessing: Cleaning and normalization of time-series data
  3. Pattern Analysis:
    • Shannon entropy calculation
    • Fourier analysis
    • LSTM-based pattern prediction
    • Anomaly detection
  4. Visualization: Comprehensive plotting of results

Results

Analysis results are saved in the results/ directory with the following structure:

  • {star_id}_analysis.npz: Numerical results and statistics
  • {star_id}_plots.png: Visualization plots
  • models/{star_id}_model.pt: Trained LSTM model

Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Open a Pull Request

Citation

If you use this code in your research, please cite:

@article{stellarnet2024,
  title={StellarNet: Investigating Information Processing Patterns in Stellar Emissions},
  author={Agora Lab AI, Kye Gomez},
  journal={arXiv preprint arXiv:2024.xxxxx},
  year={2024}
}

License

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

Acknowledgments

  • NASA's TESS and Kepler missions for providing stellar data
  • The lightkurve team for their excellent data access tools
  • The astropy community for their comprehensive astronomy tools

Contact

Want Real-Time Assistance?

Book a call with here for real-time assistance:

⭐ Star us on GitHub if this project helped you!