SETINet: AI-Driven Framework for Extraterrestrial Signal Detection

Overview

SETINet is a state-of-the-art framework for analyzing astronomical data to detect potential technosignatures of extraterrestrial intelligence. This project implements a deep learning approach to process and analyze radio telescope data, utilizing convolutional neural networks optimized for signal detection in spectral data.

Key Features

• 🔭 Automated data collection from multiple radio telescope sources
• 🤖 Deep learning-based signal detection and classification
• 📊 Real-time data processing and analysis pipeline
• 📈 Comprehensive visualization and monitoring tools
• 🔍 Advanced signal processing and noise reduction
• 💾 Efficient data management and model checkpointing

System Architecture

graph TD
    subgraph Data Pipeline
        A[Astronomical Data Sources] --> B[DataFetcher]
        B --> C[Raw Data Storage]
        C --> D[SignalProcessor]
        D --> E[Processed Data]
    end

    subgraph ML Pipeline
        E --> F[SETIDataset]
        F --> G[DataLoader]
        G --> H[SETINet Model]
    end

    subgraph Training Pipeline
        H --> I[Trainer]
        I --> J[Model Checkpoints]
        I --> K[TensorBoard Logs]
        I --> L[Training Metrics]
    end

    subgraph Model Architecture
        M[Input Layer] --> N[Conv2D + ReLU + MaxPool]
        N --> O[Conv2D + ReLU + MaxPool]
        O --> P[Conv2D + ReLU + MaxPool]
        P --> Q[Flatten]
        Q --> R[Dense + ReLU]
        R --> S[Dropout]
        S --> T[Output Layer]
    end

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Data Pipeline

graph TD
    A[Astronomical Data Sources] --> B[DataFetcher]
    B --> C[Raw Data Storage]
    C --> D[SignalProcessor]
    D --> E[Processed Data]

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Model Architecture

The SETINet model employs a deep convolutional neural network architecture optimized for spectral data analysis:

Input Layer (1 x 1024 x 1024)
    │
    ▼
Conv2D(32) + ReLU + MaxPool
    │
    ▼
Conv2D(64) + ReLU + MaxPool
    │
    ▼
Conv2D(128) + ReLU + MaxPool
    │
    ▼
Flatten
    │
    ▼
Dense(512) + ReLU
    │
    ▼
Dropout(0.5)
    │
    ▼
Output Layer (2)

Installation

Prerequisites

• Python 3.8+
• CUDA-capable GPU (recommended)
• 16GB+ RAM

Setup

1. Clone the repository:

git clone https://github.com/Agora-Lab-AI/SETINet.git
cd SETINet

2. Create and activate a virtual environment:

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

3. Install dependencies:

pip install -r requirements.txt

Usage

python main.py

Contributing

We welcome contributions! Please see our CONTRIBUTING.md for guidelines.

Citation

If you use SETINet in your research, please cite our paper:

@article{setinet2024,
  title={SETINet: Deep Learning Framework for Extraterrestrial Signal Detection},
  author={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

• Breakthrough Listen Initiative for providing open-source data
• Green Bank Observatory for radio telescope data access
• The SETI research community for valuable feedback and contributions

## 📬 Contact

• 🌐 Website: https://agoralab.ai
• 🐦 Twitter: @AgoraLabAI
• Twitter: @kyegomez
• Email: [email protected]

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