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

=== 1. Find an accurate refractive index dataset n(λ) for the medium in question (pure water at the wavelengths of interest, or a plasma dispersion model if you want hot plasmas). ===
# Identify an appropriate spectrum to weight with: - a black-body at an appropriate T, OR - an atomic emission spectrum (line list) if you want the atomic-line hypothesis to dominate, OR - a mixture (continuum + strong lines).
# Compute δmin(λ) precisely from the true n(λ), then compute the weighted mean. If the spectrum has a pronounced peak near a wavelength where δmin(λ) ≈ 137.5°, then the weighted mean can shift by fractions of a degree — the sort of 0.3–0.6° you are asking about is plausible with the right spectral shape.
# Repeat the same exercise with the geometry that QAT highlights at atomic scales (e.g., mapping e², h/2π, r² geometry into a spherical scattering or boundary condition) to see if a small systematic offset emerges that maps numerically to the fine-structure constant difference.

In short: the mechanism (spectrum weighting a spherical geometric deflection) is plausible. Whether it fits the exact small number (~0.47°) needs accurate dispersion and spectral inputs.