Convergence Mechanism

modeling diverse nanoscale phenomena, including neutrino oscilla-
tions, dipole moments, molecular junction currents, optical intensi-
ties, and quantum interference of identical photons. The model em-
ploys a gate-dependent α(VG) for molecular junctions, a wavelength-
dependent α(c) for optical intensities, a hybrid time delay-dependent
form for quantum interference, and an asymmetry parameter κ = 2.3.
It incorporates a convergence mechanism to model particles in su-
perposition as waves, stabilized to prevent energy dissipation, with a
non-linear term triggering reconvergence into particle-like states dur-
ing measurement. Using synthetic quantum interference data (τ =
384.000–384.249 ps), the mechanism achieves strong wavefunction lo-
calization (ϵ = 10−1 eV) while reproducing g(2)(τ ), suggesting a phys-
ical basis for the measurement problem. It is tested against datasets
from neutrino oscillations [1], dipole moments and molecular junc-
tion currents [2], optical intensities from impedance spectroscopy [3],
and quantum interference from GaAs quantum dots [4]. The model
achieves acceptable fits across all datasets: neutrino (χ2/dof ≈ 1.5,
max deviation 4.01σ), dipole moment (χ2/dof ≈ 0.8–1.7, max de-
viation 3.80σ), molecular junction (χ2/dof ≈ 1.3–1.7, max deviation
4.26σ), optical intensity (χ2/dof ≈ 1.5–1.6, max deviation 4.98σ), and
quantum interference (χ2/dof ≈ 2–3, max deviation 3.2σ) using a hy-
brid model with a Gaussian term. The asymmetry parameter κ = 2.3
accurately predicts ratios across systems. The theory performs excep-
tionally for transverse dipoles and adequately for all other datasets,
with minor challenges for longitudinal dipoles, demonstrating robus