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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.

• 🔭 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

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

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

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)

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

1. Clone the repository:

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

2. Create and activate a virtual environment:

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

3. Install dependencies:

pip install -r requirements.txt

python main.py

We welcome contributions! Please see ourCONTRIBUTING.md for guidelines.

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}}

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

• 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

• 🌐 Website:https://agoralab.ai
• 🐦 Twitter:@AgoraLabAI
• Twitter:@kyegomez
• Email:kye@swarms.world

Book a call with here for real-time assistance:

⭐ Star us on GitHub if this project helped you!