Discrete Sampling Geometry: A Rigorous Axiomatic Reconstruction

This paper establishes a rigorous axiomatic system for discrete sampling geometry. The system is built upon a unique existence — the unit interval I = [0,1] equipped with an involution σ(x)=1−x — and three selective axioms: Symmetry Priority, Minimality Principle, and Parameter Uniqueness. We first prove the categoricity of this axiomatic system, demonstrating that all models are uniquely isomorphic. From this foundation, we rigorously construct the unique standard model, deriving a complete geometric and algebraic chain: the natural numbers N, Z, R → the circle S¹ → the sphere S² → the dihedral group D₁₂ → a discrete curvature spectrum {0,2,5} → the Lie algebra u(1)⊕su(2)⊕su(3) → space-time S³×R → the unified Einstein–Yang–Mills dynamics. Key results include: (i) The absolute length scale is uniquely fixed by the intrinsic length of the quotient space M = I/σ, yielding a sphere S² with circumference C=1 and constant curvature K=4π². (ii) The dihedral group D₁₂ emerges as the unique maximal symmetry group on the 12-point equi-partition of the great circle. (iii) Under the refinement limit, the operator algebra on the 12-dimensional regular representation subspace V₋ contracts to the compact Lie algebra u(1)⊕su(2)⊕su(3), with dimensions 1+3+8=12 uniquely determined by the irreducible representation structure of D₁₂. (iv) The physical space-time S³×R of dimension 3+1 emerges as the minimal manifold carrying the SU(2) gauge bundle. Each step in the construction chain is uniquely forced by the axioms. The theory admits no free parameters, no external choices, and no empirical fitting. The paper forms a closed categorical axiomatic system whose fundamental principle is: to exist is to be unique.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-31
DOI
https://doi.org/10.5281/zenodo.20471836
Primary Topic
Markov Chains and Monte Carlo Methods
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Discrete Sampling Geometry: A Rigorous Axiomatic Reconstruction

Cheng Xi
Zenodo (CERN European Organization for Nuclear Research)
Markov Chains and Monte Carlo Methods
preprint

Discrete Sampling Geometry: A Rigorous Axiomatic Reconstruction

Cheng Xi
preprint en

Abstract

This paper establishes a rigorous axiomatic system for discrete sampling geometry. The system is built upon a unique existence — the unit interval I = [0,1] equipped with an involution σ(x)=1−x — and three selective axioms: Symmetry Priority, Minimality Principle, and Parameter Uniqueness. We first prove the categoricity of this axiomatic system, demonstrating that all models are uniquely isomorphic. From this foundation, we rigorously construct the unique standard model, deriving a complete geometric and algebraic chain: the natural numbers N, Z, R → the circle S¹ → the sphere S² → the dihedral group D₁₂ → a discrete curvature spectrum {0,2,5} → the Lie algebra u(1)⊕su(2)⊕su(3) → space-time S³×R → the unified Einstein–Yang–Mills dynamics. Key results include: (i) The absolute length scale is uniquely fixed by the intrinsic length of the quotient space M = I/σ, yielding a sphere S² with circumference C=1 and constant curvature K=4π². (ii) The dihedral group D₁₂ emerges as the unique maximal symmetry group on the 12-point equi-partition of the great circle. (iii) Under the refinement limit, the operator algebra on the 12-dimensional regular representation subspace V₋ contracts to the compact Lie algebra u(1)⊕su(2)⊕su(3), with dimensions 1+3+8=12 uniquely determined by the irreducible representation structure of D₁₂. (iv) The physical space-time S³×R of dimension 3+1 emerges as the minimal manifold carrying the SU(2) gauge bundle. Each step in the construction chain is uniquely forced by the axioms. The theory admits no free parameters, no external choices, and no empirical fitting. The paper forms a closed categorical axiomatic system whose fundamental principle is: to exist is to be unique.

Zenodo (CERN European Organization for Nuclear Research)
Markov Chains and Monte Carlo Methods
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.