Phenomenological Niranjan Cosmic Repulsion Effect Field Theory

This preprint formalizes the exact first-order linear perturbation theory and establishes unified cosmic background optimization benchmarks for the Phenomenological Niranjan Cosmic Repulsion Effect Field Theory. This scalar-tensor framework introduces a late-time density-gated mechanism designed explicitly to resolve the ongoing 5-sigma Hubble tension. The structural core of the model originates from a macroscopic temporal volume-averaging of an underlying alternative sub-Planckian spatial scaling relation R = Delta M m r^1.86, translating micro-scale mass-distance couplings into a homogeneous, dark energy tracking potential. We implement a sharp Heaviside step gating function acting as a density-triggered activation switch to guarantee that the dynamic tracking field remains completely suppressed during early-universe epochs, thereby perfectly insulating pristine early-universe thermodynamics from unwanted modifications. To evaluate the mathematical and physical viability of this mechanism, the parameter space is optimized against a comprehensive multi-era joint dataset compilation including 1701 low-redshift standards, 5 independent nodes from Baryon Acoustic Oscillations, and the tightly constrained Planck Cosmic Microwave Background shift parameters. Our global numerical minimization pipeline demonstrates exceptional statistical convergence, yielding an overall minimized joint chi-square of 1585.5516 and a balanced reduced performance score of 0.9327. The gate triggers open at a localized low-redshift threshold of z_c = 1.4965, releasing a non-zero late-time dynamic dark energy amplitude Omega_dyn = 0.0158 that safely pulls the local predicted Hubble constant to H0 = 71.6355 km/s/Mpc. Finally, we map the effective field theory boundaries of the model and quantify its radiative susceptibility to one-loop quantum corrections via the Coleman-Weinberg potential. Acknowledgment: The author utilized artificial intelligence tools for LaTeX formatting support, grammatical optimization, and python simulation structuring.