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https://github.com/agora-lab-ai/setinet
SETINet is an new net for analyzing astronomical data to detect potential technosignatures of extraterrestrial intelligence.
https://github.com/agora-lab-ai/setinet
ai alien-ai astro-ai astrophysics astrophysics-research dod gia gia-ai gov ml nasa planterary-security seti spaceforce
Last synced: about 2 months ago
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SETINet is an new net for analyzing astronomical data to detect potential technosignatures of extraterrestrial intelligence.
- Host: GitHub
- URL: https://github.com/agora-lab-ai/setinet
- Owner: Agora-Lab-AI
- License: mit
- Created: 2024-11-08T18:29:43.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-08T18:34:17.000Z (about 2 months ago)
- Last Synced: 2024-11-08T19:32:02.247Z (about 2 months ago)
- Topics: ai, alien-ai, astro-ai, astrophysics, astrophysics-research, dod, gia, gia-ai, gov, ml, nasa, planterary-security, seti, spaceforce
- Language: Python
- Homepage: https://discord.com/servers/agora-999382051935506503
- Size: 26.4 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
# SETINet: AI-Driven Framework for Extraterrestrial Signal Detection
[](https://discord.gg/agora-999382051935506503) [](https://www.youtube.com/@kyegomez3242) [](https://www.linkedin.com/in/kye-g-38759a207/) [](https://x.com/kyegomezb)
[](https://www.python.org/downloads/release/python-380/)
[](https://pytorch.org/)
[](https://opensource.org/licenses/MIT)
[](https://arxiv.org/abs/)
## 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
```mermaid
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
```
### Data Pipeline
```mermaid
graph TD
A[Astronomical Data Sources] --> B[DataFetcher]
B --> C[Raw Data Storage]
C --> D[SignalProcessor]
D --> E[Processed Data]
```
### 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:
```bash
git clone https://github.com/Agora-Lab-AI/SETINet.git
cd SETINet
```
2. Create and activate a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
## Usage
```bash
python main.py
```
## Contributing
We welcome contributions! Please see our [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
## Citation
If you use SETINet in your research, please cite our paper:
```bibtex
@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](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](https://twitter.com/AgoraLabAI)
- Twitter: [@kyegomez](https://twitter.com/kyegomez)
- Email: [email protected]
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