Editing Openai/6897769e-4ee4-800f-aba5-69cca34f701c (section)

=== 1. Dispersion: I used a simple Cauchy form (A + B/λ²). That’s fine for a toy run but not precise enough if you want differences ≲0.5°. Use high-precision refractive index tables (Hale & Querry, refractiveindex.info). You suggested that earlier — I strongly recommend that for the next run. ===
# Line strengths & plasma lines: I used simple amplitudes. Real stellar/plasma line strengths vary widely and may be dominant in different physical environments. If QAT expects plasma emission to be relevant, we must include plasma line sets and appropriate weights.
# Wave optics / droplet size: geometric optics is accurate for large raindrops, but for small scatterers wave effects (interference) shift peak intensity positions slightly. If you want angle precision ≲0.5°, include size effects.
# Polarization and temperature choices: polarization and observer geometry matter in real rainbow brightness profiles. Also different source temperatures shift the spectral weighting (I used 5800 K as a Sun proxy, but other temperatures are easy to try).
# A fully physical mapping to α: showing a numerical coincidence is one thing; deriving α from QAT geometry requires showing how e, ħ, c, ε₀ emerge together from the same geometry. My current computation does not do that — it simply shows that spherical refraction + realistic spectra produce angles near the golden-angle region.