Stellar Insight Labs

Illuminating new worlds through intelligent discovery.

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Inspiration

Space exploration has always been a quest to understand the unknown. But as telescopes like NASA’s Kepler and TESS generate terabytes of photometric data daily, manual classification of exoplanets has become nearly impossible. The inspiration for Stellar Insight Labs was born from this challenge — combining AI and astrophysics to accelerate the discovery of new worlds. Our goal was to democratize access to space data and empower researchers and enthusiasts alike through intelligent automation.

What it does

Stellar Insight Labs is an AI-driven exoplanet detection and classification platform that transforms raw NASA data into scientific insights. It:

Analyzes light curves and radial velocity data to identify possible exoplanet transits.

Uses CNN-LSTM hybrid models to classify candidates with high accuracy.

Explains predictions using SHAP and LIME for transparent, trustable AI.

Computes a Habitability Score based on stellar and planetary parameters.

Integrates real-time NASA APIs for updated mission data.

Offers an interactive 3D orbital visualization built with Three.js.

Essentially, it’s an AI assistant for astronomers, helping turn raw starlight into discovery.

How we built it

We engineered an end-to-end AI pipeline:

Data Pipeline: Python, Pandas, NumPy for data ingestion and preprocessing from NASA’s Exoplanet Archive and MAST APIs.

Machine Learning Models: Scikit-learn, LightGBM, and TensorFlow for classification; CNN-LSTM architectures for time-series photometric data.

Backend: FastAPI and Flask for API handling and model predictions.

Frontend: React, TailwindCSS, Framer Motion, and Three.js for UI/UX and 3D visualization.

Deployment: Frontend hosted on Netlify; backend deployed via Render.

Database: PostgreSQL for managing raw and processed datasets.

Challenges we ran into

Handling massive datasets from Kepler and TESS while maintaining low latency.

Designing interpretable AI models for scientific transparency.

Integrating multiple APIs while ensuring real-time synchronization.

Rendering interactive 3D orbits smoothly within a web browser.

Balancing accuracy and explainability without compromising computational speed.

Accomplishments that we're proud of

Achieved 99.2% detection accuracy across validated datasets.

Built a scalable, modular architecture capable of expanding beyond exoplanet analysis.

Designed a user-friendly interface bridging astronomy and AI.

Created a Habitability Score Predictor, one of the first of its kind in an open-source context.

Enabled real-time NASA data integration with live inference.

Delivered an uptime reliability of 99.9% with an average latency of just 0.01 seconds per data point.

What we learned

The power of AI interpretability in gaining scientific trust.

How cross-disciplinary innovation (AI + astrophysics) can uncover new possibilities.

The importance of data reliability and validation for credible research outcomes.

Collaborative development across frontend, backend, and AI pipelines for a cohesive product.

What's next for Stellar Insight Labs

Expanding beyond exoplanets into asteroid tracking and climate satellite analytics.

Launching an open-source API for researchers and educators worldwide.

Introducing a citizen-science portal for global collaboration in planet classification.

Implementing real-time anomaly detection for transient celestial events.

Continuing our vision to democratize space data — making AI-powered space exploration accessible to everyone, everywhere.

Built With

  • backend)
  • flask-frontend:-react
  • framer-motion
  • javascript-libraries:-pandas
  • languages:-python
  • lightgbm
  • lime
  • mast-api-visualization:-shap
  • numpy
  • render
  • scikit-learn
  • tailwindcss
  • tensorflow-backend:-fastapi
  • three.js-(3d-orbit-visualizer)-deployment:-netlify-(frontend)
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