WHAT ARE EXOPLANETS?
Planets orbiting stars beyond our solar system, discovered by observing the tiny dips in starlight they cause when passing in front of their star.
🪐 Definition
Exoplanets are planets that orbit stars outside our solar system. Over 5,000 have been confirmed, with thousands more candidates waiting for verification.
🔭 Detection Method
The transit method detects exoplanets by measuring the decrease in starlight when a planet passes between the star and our observation satellite.
🛰️ NASA Missions
Kepler, K2, and TESS missions have collected massive amounts of data, enabling the discovery of thousands of new exoplanets through transit observations.
A UNIVERSE OF WORLDS
Confirmed exoplanets come in a wide variety of sizes and types, many of which are unlike anything found in our own solar system.
Gas Giants
Large planets composed mostly of helium and hydrogen, similar to Jupiter and Saturn. "Hot Jupiters" are gas giants that orbit very close to their parent star.
Super-Earths
Potentially rocky planets that are more massive than Earth but lighter than Neptune. They are one of the most common types of exoplanets discovered.
Neptune-like
Planets similar in size to Neptune or Uranus, likely with a mixture of rock, ice, and a thick atmosphere of hydrogen, helium, and methane.
NASA DATASETS
Our AI model is trained on high-quality, open-source data from premier planet-hunting missions. These datasets contain thousands of observations with features like orbital period, transit depth, stellar radius, and more.
UNDERSTANDING THE LABELS
Not every dip in starlight is a planet. The raw data is classified into three categories, which our AI learns to predict:
- Confirmed Exoplanet: A signal that has been rigorously verified by scientists to be a genuine planet orbiting another star.
- Planetary Candidate: A signal that shows all the characteristics of an exoplanet transit but is awaiting further observation and confirmation.
- False Positive: A signal that mimics a transit but is caused by other phenomena, such as a background eclipsing binary star system, starspots, or instrumental noise.
CHALLENGES IN DETECTION
Identifying exoplanets is a monumental task filled with complex challenges that AI is uniquely suited to solve.
- Manual Analysis: Most exoplanets were identified manually, which is time-consuming and labor-intensive.
- Massive Datasets: Missions generate terabytes of data. Manual review cannot keep pace with this volume.
- False Positives: Many candidates are false positives caused by stellar activity or instrumental noise.
- Hidden Discoveries: Thousands of potential exoplanets remain hidden in existing data, waiting for advanced analysis.
DATA PREPROCESSING: FROM RAW TO READY
Raw data from space telescopes is messy. Before feeding it to our AI, we perform several crucial preprocessing steps to clean and standardize it.
- Handling Missing Values: We use statistical methods (like mean or median imputation) to fill in gaps in the data where observations are missing.
- Feature Scaling: We normalize numerical features (e.g., stellar radius, orbital period) to a common scale. This prevents features with larger values from unfairly dominating the model's learning process.
- Feature Selection: We focus on the most impactful features for classification, such as transit duration, transit depth, and orbital period, to reduce complexity and improve performance.
OUR SOLUTION: AI/ML AUTOMATION
We developed an advanced AI/ML model trained on NASA's datasets that automatically analyzes light curve data and classifies results with high accuracy.
- Automated analysis of thousands of light curves in minutes.
- Real-time classification: Confirmed, Candidate, or False Positive.
- Analyzes 15+ features including orbital period, transit duration, and planetary radius.
- Web interface for researchers to upload and analyze new data.
PREDICTION WORKFLOW
Our tool makes this complex process simple. A researcher or enthusiast can upload their own data and get an instant classification.
- User uploads a CSV file containing new transit data (orbital period, radius, etc.).
- Our backend preprocesses the uploaded data using the same pipeline as the training data.
- The trained AI model predicts a label for each observation.
- The tool outputs the classification (e.g., "Planetary Candidate") along with a confidence score.
MACHINE LEARNING MODELS TESTED
We tested multiple algorithms to find the best performer for classifying exoplanet candidates.
🏆 XGBoost
Excels at handling imbalanced datasets and provides superior accuracy.
🌲 Random Forest
Ensemble method using multiple decision trees for reliable classification.
📊 Support Vector Machine
Effective in high-dimensional feature spaces with clear margins.
WHY XGBOOST IS THE BEST MODEL
XGBoost consistently outperformed other models in key areas crucial for astronomical data analysis.
| Feature | XGBoost | Random Forest | SVM |
|---|---|---|---|
| Accuracy | 95.8% | 92.4% | 90.6% |
| Imbalanced Data Handling | Excellent | Good | Fair |
| Overfitting Control | Excellent | Good | Good |
| Missing Value Handling | Automatic | Good | Manual |
DATA INSIGHTS & DISCOVERY TRENDS
Visualizing exoplanet discoveries and distribution patterns reveals key trends in our search.
REAL-WORLD IMPACT
Our AI model is transforming exoplanet research by making it faster, more accurate, and more accessible.
⚡ Faster Analysis
Process thousands of light curves in minutes instead of months.
🎯 High Accuracy
Reduces false positives and ensures reliable confirmations.
🔍 New Discoveries
Uncovers exoplanets missed during manual analysis.
Ready to Discover New Worlds?
Explore our interactive tools and experience the future of exoplanet classification.
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