PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
December 7, 202510 citationsOpen Access

The Topology of Mass: Knot Geometry and Lepton-Meson Hierarchies in Causal Latency Theory

View Full Paper
SDSandner, Daniel

Key Points

  • Mass gaps among leptons were identified through causal latency theory, emphasizing the muon's significance.
  • The standard model's treatment of masses and generations relies on arbitrary coupling constants, which this theory addresses.
  • Exploration of mass gaps and lepton hierarchy used the 3D Dirac equation in causal latency theory to uncover new insights.
  • The results imply that theoretical models may need to re-evaluate coupling constants and mass generation mechanisms.],

Abstract

The Standard Model of particle physics classifies fundamental particles with high precision but treats their masses and generation structure as free parameters. Why there are exactly three generations of leptons, and why the Muon is 207 times heavier than the electron, remains explained only by arbitrary coupling constants. We propose that the particle spectrum emerges from Causal Latency Theory (CLT) as topological excitations of a single vacuum standing wave. Building on the "Causal Knot" model P3, we treat fundamental particles as topological excitations of the vacuum. By solving the 3D Dirac equation within a self-confining causal potential, we demonstrate that: (1) The Electron (1s), Muon (2p), and Tau (3d) correspond to distinct topological winding numbers () of the causal knot; (2) The massive energy gaps between generations arise from a Causal Impedance Feedback loop where topological winding increases the local refractive index of the vacuum; and (3) The Muon mass (105 MeV) is derivable from the differential geometry of a Trefoil Knot (3₁), where the vacuum shear stress generated by torsion accounts for the mass gap. Finally, we provide a geometric basis for the Koide Formula, suggesting that lepton masses are related by exact phase constraints.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Sandner, Daniel (2025) studied this question.

synapsesocial.com/papers/694020f72d562116f28fb344https://doi.org/10.5281/zenodo.17844205
Ask AI
Helpful
Bookmark
Share
View Full Paper