PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 7, 20260 citationsOpen Access

Ab Intus — Geometric Self Theory Mathematical Framework

View Full Paper
SSS SchultzBEB ErranteJHJ Hosain

Key Points

  • The research aims to establish a parameter-free Theory of Everything based on discrete number theory.
  • Develops a mathematical framework using a topological network instead of continuous path integrals.
  • Derives fundamental observables like space, time, mass, and gravity from a single polynomial's structural properties.
  • Utilizes observational cosmological data to validate predictions about dark matter and gauge symmetries.
  • Successfully unifies the Standard Model gauge symmetries without arbitrary constants.
  • Predicts a dark matter sector and provides cosmological fractions that align with observational evidence.
  • Offers an algebraic solution to existing challenges in quantum gravity and particle physics.

Abstract

This manuscript presents a completely parameter-free Theory of Everything derived entirely from discrete number theory. By replacing continuous path integrals with a deterministic, topological network, the framework natively generates the fundamental observables of physics—space, time, mass, and gravity—from the structural properties of a single polynomial. It successfully unifies the Standard Model gauge symmetries, predicts a confined dark matter sector, and yields cosmological fractions that precisely match observational data. Requiring no arbitrary constants, no renormalization, and utilizing only a single physical measurement to set the universe's energy scale, this paper offers a rigorously algebraic resolution to the foundational problems of modern quantum gravity and particle physics.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Schultz et al. (2026) studied this question.

synapsesocial.com/papers/69d49fc5b33cc4c35a2283b1https://doi.org/10.5281/zenodo.19431796
Ask AI
Helpful
Bookmark
Share
View Full Paper