Built on NASA's Kepler and TESS mission data, this tool analyzes over 100 measured parameters per object to determine if a signal is a confirmed planet, candidate, or false positive.
Small animated plot showing a brightness dip. Hover the markers to reveal: Transit Depth, Duration, Repetition Interval.
“When a planet passes in front of its star, it blocks some light. AI looks for this repeated dimming pattern.”
Just a toy classifier to build intuition — not the full model yet.
Transit Depth (%)
Repetition Period (Days)
The AI would consider this:
depth ≈ 0.20%, period ≈ 10 days
How the AI Makes Its Decision
Feature groups with their relative influence. Expand a group to view the raw columns.
Transit Event Geometry & Timing
Describes how the planet crosses the star.
Columns in this group
Orbital & Physical Characteristics
Planet’s orbit and estimated physical size/temperature.
Columns in this group
Stellar Properties (Inputs from Host Star)
AI infers planetary likelihood based on host star context.
Columns in this group
Signal Quality / Detection Confidence
Measures how reliable the transit signal is.
Columns in this group
Stellar Motion & Distance
Helps eliminate background blends or nearby star contamination.
Columns in this group
Photometric Magnitudes (Brightness Across Instruments)
Determines data quality from various sensors.
Columns in this group
Planetary System Multiplicity & Architecture
Multi-planet systems tend to have higher exoplanet likelihood.
Columns in this group
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For users who want to manually input one object or test AI behavior interactively.
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