An innovative, AI-powered platform aimed at revolutionizing the detection and understanding of exoplanets by leveraging advanced machine learning techniques and data from NASA's TESS, Kepler, and K2 missions.
DeepSpaceAI provides users with the ability to explore and analyze exoplanet data in a highly interactive and educational environment. By utilizing artificial intelligence, DeepSpaceAI can identify exoplanets from raw mission data with remarkable accuracy.
- AI-Powered Analysis: Advanced machine learning models trained on NASA mission data
- Real-time Discovery Updates: Stay current with the latest exoplanet discoveries
- Interactive Visualization: Explore star systems and planetary transits in real-time
- Comprehensive Filtering: Filter by star types (giant, small, rocky) and mission sources (Kepler, TESS, K2)
- Detailed Information: Access rich data about stars including mass, radius, temperature, and position
- Transit Visualization: Watch the classic dip in brightness during planetary transits
- Hyperparameter Tuning: Adjust learning rates, batch sizes, and epochs to optimize performance
- Performance Metrics: Monitor training status and model improvements
- Educational Insights: Understand the AI process behind exoplanet detection
- NASA TESS Mission: Transiting Exoplanet Survey Satellite data
- Kepler Mission: Legacy exoplanet discovery data
- K2 Mission: Extended Kepler mission observations
- Node.js (v18 or higher)
- npm or pnpm package manager
- Clone the repository:
git clone https://github.com/your-username/DeepSpaceAI.git
cd DeepSpaceAI- Install dependencies:
npm install
# or
pnpm install- Run the development server:
npm run dev
# or
pnpm dev- Open http://localhost:3000 in your browser.
- Frontend: Next.js 14 with TypeScript
- UI Components: Tailwind CSS with shadcn/ui
- 3D Graphics: Three.js for space visualization
- Data Processing: Custom AI models for exoplanet detection
- State Management: Zustand for application state
DeepSpaceAI/
├── app/ # Next.js app directory
├── components/ # React components
│ ├── ui/ # Reusable UI components
│ ├── exoplanet-detection.tsx
│ ├── star-system.tsx
│ └── ...
├── data/ # Static data files
├── hooks/ # Custom React hooks
├── lib/ # Utility functions and stores
└── public/ # Static assets
- Exoplanet Detection: AI-powered analysis of transit data
- Star System Visualization: Interactive 3D space exploration
- Hyperparameter Tuning: Model optimization interface
- Real-time Updates: Live discovery notifications
- Educational Tools: Learning resources and explanations
DeepSpaceAI uses key planetary parameters to train AI models:
- Orbital period
- Transit depth
- Transit duration
- Stellar properties (mass, radius, temperature)
These models can distinguish exoplanets from false positives with high accuracy, making the platform a valuable tool for both research and education.
DeepSpaceAI democratizes access to cutting-edge space research by:
- Making exoplanet discovery accessible to everyone
- Providing hands-on experience with AI in space science
- Offering interactive learning tools for students and educators
- Encouraging public participation in space exploration
We welcome contributions from scientists, developers, educators, and space enthusiasts! Please see our contributing guidelines for more information.
This project is licensed under the MIT License - see the LICENSE file for details.
- NASA for providing the TESS, Kepler, and K2 mission data
- The exoplanet research community for their groundbreaking work
- Open source contributors who make projects like this possible
For questions, suggestions, or collaboration opportunities, please reach out through our GitHub issues or contact the development team.
DeepSpaceAI - Making space exploration accessible, educational, and impactful for everyone. 🌌✨