none

None: Python's null. The void. The unknowable.

A research tool that does a computational deep dive into what Artemis II photographed — and specifically what Space Force (USSF) might be studying from that imagery.

Artemis II flew April 1–11, 2026. The crew (Wiseman, Glover, Koch, Hansen) photographed the far side of the Moon, a live in-space solar eclipse with Venus visible, micrometeoroid impact flashes on the lunar surface, auroras observed from deep space, and Earthset/Earthrise from lunar distance.

The question this program explores: What did Artemis II actually photograph, and what in that imagery would interest Space Domain Awareness?

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Install

pip install -r requirements.txt

Usage

# Full pipeline: fetch → analyze → report
python none.py --all

# Individual steps
python none.py --fetch      # Download images from NASA Image Library
python none.py --analyze    # Analyze for anomalies (brightness, entropy, edges, flash events)
python none.py --report     # Generate report.md

What it does

fetch.py

Queries the NASA Image and Video Library API for Artemis II imagery (no API key required). Downloads up to 20 images to ./images/. Tries multiple queries if the primary returns no results.

analyze.py

For each image:

• Flash detection — flags images where >0.1% of pixels exceed the 99.5th brightness percentile (potential micrometeoroid impact flashes)
• Shannon entropy — measures information density (high entropy = complex scene; anomalously low = deep-space void)
• Edge detection — Sobel-approximated gradient for high-contrast surface features
• Anomaly score (0–100) — weighted composite

report.py

Generates report.md with:

• Mission timeline
• Full image inventory
• Top 5 highest anomaly-score images with analysis notes
• What USSF Space Domain Awareness would care about (MMOD tracking, far-side intelligence, solar corona, auroral environment)
• "What we cannot know" — acknowledging what remains classified

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Output

none/
  images/           # downloaded NASA imagery
  report.md         # generated analysis report
  .metadata_cache.json
  .analysis_cache.json

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The name

none is Python's null. It's what you get when a function returns nothing, when a value is absent, when a variable exists but contains the void. It's the right name for a program exploring the space between what was photographed and what was released — the gap that is itself the signal.

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No classified sources were accessed. All imagery is from public NASA archives.