Convergence Field Theory

The Convergence Field theory provides a unified framework for 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 robust universality. Future work includes validating the hybrid model and convergence mechanism with real quantum interference data from [4], optimizing the non-linear term, and exploring additional datasets to further refine the model.