A Visible-Survival Residual Framework for Blind PMNS-Based Searches of Hidden Neutrino Mass-Origin Signatures

This upload contains a computational proof-of-method manuscript and supporting materials for a Visible-Survival Dimensional Framework (VSDF) -inspired residual search in simulated neutrino-like data. The study introduces a blind PMNS-based residual-search workflow for testing whether injected hidden-cause signatures can be detected while null baselines are rejected. The implementation uses a three-flavor PMNS vacuum baseline, blind simulated datasets, null tests, robustness checks, survival-scaling recovery, and a starter detector-systematics/covariance layer. In 100 blinded simulated datasets, the method produced zero false positives on null data and detected 52 of 75 injected hidden-like datasets, giving FPR = 0. 000, TPR = 0. 693, precision = 1. 000, recall = 0. 693, and F1 = 0. 819. The VSDF survival-scaling test recovered Dₑff = -0. 500 for k = 0. 5. This work is a computational proof-of-method only. It does not claim evidence for real hidden dimensions, sterile neutrinos, Majorana neutrinos, or the physical origin of neutrino mass.