PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
June 4, 20260 citationsOpen Access

SHUM V3.2: A Discrete FCC Lattice Framework for Cosmological Bounce and Quantum Geometric Mass

View Full Paper
YAYeerbate Adaerbaike

Key Points

  • This research aims to propose a unified model for cosmological bounce and the generation of particle mass using a discrete FCC lattice framework.
  • Introduced the SHUM V3.2 model with core-shell dual field and geometric repulsion reversal (G = -1.71).
  • Developed the model to reinterpret the Big Bang as a bounce, resolving the singularity in general relativity.
  • Utilized the double group representations of O_h to derive the geometric origin of spin states.
  • Resolved the singularity problem by redefining the Big Bang as a cosmological bounce.
  • Identified particles as topological standing waves on the FCC lattice with a spatial leakage rate of 12/19.
  • Derived a fundamental dimensionless mass baseline of approximately 0.7947.

Abstract

This preprint proposes the SHUM V3. 2 model, a theoretical framework that unifies macroscopic cosmological bounce and microscopic particle mass generation through a discrete Face-Centered Cubic (FCC) lattice spacetime. By introducing a "core-shell dual field" and a geometric repulsion reversal (G = -1. 71), the model resolves the singularity problem in general relativity, reinterpreting the Big Bang as a bounce from a previous collapsing phase. Microscopically, particles are modeled as topological standing waves on the FCC lattice with a spatial leakage rate of 12/19. Using the double group representations of Oₕ, we derive the geometric origin of spin-1/2 and spin-1 states, yielding a fundamental dimensionless mass baseline of 12/19 0. 7947. The paper also explicitly outlines four theoretical limitations (spin-orbit coupling, discrete gauge symmetry, fermion doubling, and generation structure) to guide future research in discrete quantum gravity.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Yeerbate Adaerbaike (2026) studied this question.

synapsesocial.com/papers/6a211852d499ed480b170e5bhttps://doi.org/10.5281/zenodo.20517449
Ask AI
Helpful
Bookmark
Share
View Full Paper