Hi HN,

I'm a systems architect and security researcher by trade (normally breaking things in crypto/infra), but I've spent the last year applying a software engineering approach to the Dark Matter problem.

Instead of adding new particles, I built a simulation based on a 5D Holographic scalar field. The math is dense, so I wrote a verification pipeline in Python to check the results against real galaxy data.

The crazy part? It fits the 175 SPARC galaxies better than Newton (R^2 ~ 0.9) without needing Dark Matter particles. The verifiable part: The model predicts a specific "hum" in atomic clocks at 2.159 mHz (a bulk breathing mode). If we don't find that signal, my code is wrong. If we do, it's a huge deal.

About this repo:

This isn't the full heavy solver (which is a mess of stiff ODE integration), but the Reproducibility Pack. It allows you to run the stats and generate the charts from the raw public data yourself to verify I didn't cherry-pick the results.

I’m releasing this via Zenodo/GitHub because the academic peer-review process takes forever, and I want the "hacker" community to tear it apart (or verify it) first.

Feedback on the SPARC_VERIFICATION logic is welcome.