Patterns Beneath the Stars: How Exoplanet Features Reveal Their Identity

Seeing the Universe Through Statistics

In our previous post, we mapped the Kepler sky distribution and explored how logistic regression identifies exoplanet types using machine learning.
Now, we dive deeper — looking at how key numerical features differ across planetary labels using boxplots.

Each plot compares feature distributions across the four classification labels:

• 0 → Non-planetary objects

• 1 → Planet candidates

• 2 → Confirmed exoplanets

• 3 → Rare/uncertain cases

Interpreting the Patterns

1. Principal Components (PC1–PC3)

These three PCA features summarize dozens of original telescope parameters.

• Labels 0 and 1 show a wide variation — suggesting mixed stellar properties.

• Label 2 (confirmed planets) clusters more tightly, implying stable photometric signals.

• Label 3 barely varies, reinforcing the data scarcity observed in our confusion matrix.

Takeaway: PCA effectively compresses light-curve patterns; confirmed exoplanets tend to occupy more consistent ranges within this reduced feature space.

2. Planet Radius

Here, the distribution stretches dramatically for label 2, showing several massive objects — likely gas giants or hot Jupiters.
Labels 0 and 1 remain near zero, aligning with non-planetary or small candidates.

Takeaway: Planet size remains one of the strongest predictors of true exoplanet classification.

3. Orbital Period

Most values cluster below 10,000 days, but label 2 includes rare long-period planets.
The outliers emphasize that Kepler was biased toward short-period detections, as they produced more transits within its mission timeframe.

Takeaway: Mission design shapes what we can observe — short orbits dominate because they are easier to confirm.

4. Stellar Temperature (star_teff)

Across all labels, most stars lie between 5,000 and 7,000 K — near solar-type.
However, slight skews for labels 0 and 1 suggest detection noise or subgiant contamination.

Takeaway: Stellar environments affect detection accuracy; hotter stars produce more variability, which complicates classification.

5. Kepler Magnitude (kep_mag)

Brightness plays a clear role: brighter stars (lower magnitudes) are more likely to yield confident detections (label 2).
Dimmer stars result in uncertain or false positives (label 1).

Takeaway: Data quality is literally measured in light. The fainter you go, the fuzzier your predictions.

Bringing It All Together

This statistical fingerprint reinforces our earlier logistic regression findings:

These patterns remind us that machine learning is only as good as its inputs.
The boundaries between stars and planets are not always numerical — sometimes, they’re observational.

Policy and Research Implications

For the data science and astrophysics community:

Acknowledgements

• Data Source: NASA Kepler Exoplanet Archive (via Kaggle).

• Analysis: Conducted by Collins Odhiambo Owino under DatalytIQs Academy – Data Science & Space Analytics Division.

• Tools Used: Python (seaborn, pandas, matplotlib, scikit-learn).

• Institutional Credit: DatalytIQs Academy – advancing knowledge at the intersection of Data, Economics, and the Cosmos.

Closing Reflection

“Every planet we discover expands our view of the universe —
but every dataset we analyze expands our understanding of ourselves.”

Through data, policy, and education, DatalytIQs Academy continues to connect the dots — between stars, between models, and between minds.