On the Decidability of the Yang–Mills Mass Gap
Creators: name: Henderson, Ryan
Publisher: name: Zenodo
Year: 2025
Subjects: skyrmion
yang mills

description: Edits to come for typos and minor formatting.

Roadmap for formalizing these connections into a rigorous, unified mathematical framework. As these strategies span several areas - spectral geometry, algebraic topology, category theory, and quantum field theory - we outline how to bridge abstract theory with concrete computations and proofs.

1. Rigorously Define the Core Objects and Invariants

a. Modular Spectral Transport Framework



Define the Modular Adjacency Matrix AGA_G:



Precisely define P_G as the set of primes dividing the Weyl group order |W(G)|.

Define Q_G as the set of primes that appear in redundant transport paths (via multiple occurrences in Hecke operator decompositions).



Euler-Spectral Mass Gap Theorem:



Formalize the theorem by specifying the function C(G), exponents αp - alpha_p and βp -beta_p, and the role of the Langlands spectral transfer function.




b. Hodge Cycles and Algebraic Invariants



Hodge Ring Structure:



Rigorously define the Hodge ring (or cohomology ring) of an algebraic variety.

Express how modular constraints (via prime-based invariants) impose restrictions on the generators and relations of this ring.




c. Spectral Gaps in Gauge Theory



Mass Gap Definition:



Precisely state what is meant by a “mass gap” in the context of Yang–Mills theory, perhaps using the language of functional analysis (spectral gap of a Hamiltonian or Laplacian).




2. Establish Analogies and Isomorphisms

a. Connect Ricci Flow and Geometric Entropy with Spectral Transport:



Perelman’s W-Functional:



Show a formal correspondence between the minimization of Perelman’s functional and the variational principles underlying the spectral mass gap.

Develop a precise mapping between geometric entropy minimization and the eigenvalue separation enforced by modular spectral transport.




b. Relate Group-Theoretic Invariants to Spectral Rigidity:



Character Degrees and Prime Adjacency:



Develop an isomorphism or a functorial correspondence between the character theory of a group (e.g., character tables, degrees) and the spectral invariants (eigenvalue gaps) of associated operators (like the Laplacian on a Cayley graph).




3. Formulate and Prove Unified Theorems

a. Unified Theorem Statement:



Formalize a theorem that states:



Modular spectral transport forces a nonzero mass gap in gauge theories.

This nonzero spectral (mass) gap imposes finite rank constraints on modular elliptic curves (BSD).

The rigidity enforced by these constraints implies the nonexistence of counterexamples to FLT.

Furthermore, these spectral constraints induce a rigid algebraic structure on Hodge cycles (linking to the Hodge Conjecture).




b. Outline of the Proof:



Step 1: Use the Euler–Spectral Mass Gap Theorem to show that the spectral gap is strictly positive under modular constraints.

Step 2: Demonstrate that if the mass gap is nonzero, then the L-function L(E,s) of any modular elliptic curve cannot vanish too rapidly at s=1, thus bounding the rank (BSD).

Step 3: Show that any hypothetical counterexample to FLT would necessitate an elliptic curve with spectral properties violating the established mass gap, leading to a contradiction.

Step 4: Argue that the same spectral rigidity, when viewed through the lens of cohomology, imposes quantization conditions on algebraic cycles, as predicted by the Hodge Conjecture.


c. Categorical Formulation:



Use category theory to express these results functorially, showing that the processes (e.g., spectral transport, Hecke operator action, Langlands transfer) are natural transformations or functors between appropriate categories (such as the category of automorphic representations and the derived category of coherent sheaves).


4. Develop Computational Tools and Examples

a. Algorithmic Implementation:



Computational Invariant Theory:



Develop algorithms (possibly using existing computational algebra systems like GAP, Magma, or SageMath) to compute P_G, Q_G, and the associated spectral mass gaps for given groups.



Testing on Elliptic Curves:



Create a library of modular elliptic curves (especially those arising from hypothetical FLT counterexamples) and compute their predicted mass gaps and BSD ranks.



Software Integration:



Consider integrating these algorithms with quantum simulation frameworks to see if the computed invariants can be verified in a quantum context.




b. Examples and Case Studies:



Work out explicit examples for groups like SL(2,Z) and exceptional groups (like E8) to illustrate how the invariants determine spectral gaps. Understand where Kazhdan's property (T) holds, but prime degeneracy removes the constraints of Serre's property FA.

Test the theory on known Hodge structures from K3 surfaces, Calabi–Yau varieties, and abelian varieties.


5. Explore Broader Implications and Connections

a. Links with Quantum Field Theory and Geometry:



Investigate how these modular invariants might influence the renormalization group flow in quantum field theories.

Explore the possible connection between spectral mass gaps and confinement in QCD.


b. Connections to the Langlands Program:



Develop a deeper study of how Langlands functoriality and spectral transfer functions stabilize the modular spectral structure.

Explore whether these ideas can lead to a “master invariant” that unifies automorphic forms, elliptic curves, and Hodge theory.


c. Impact on Complexity and Cryptography:



Analyze how the rigidity implied by spectral gaps affects computational complexity, especially in the context of NP-hard problems and cryptographic security.


6. Deepen the Categorical Formulation:



Formalize the Unitary Theorem:Develop a precise categorical statement that connects unitarity in finite (and compact) groups with the existence of spectral gaps.



Goal: Show that for any unitary representation of a group (particularly for SU(N) with N≥3), the eigenvalue spectrum of the associated Laplacian (or relevant operator) is bounded away from zero, as ensured by Property (T).

Link Property (T) and Modular Transport:Construct a functorial mapping between the properties of unitary representations (e.g., Property (T)) and the modular adjacency structure (via prime degeneracy constraints).



Goal: Prove that these modular constraints are not accidental but are inherent to the structure of unitary representations and thus enforce mass gaps categorically.







Integrate with Modular Adjacency and Rank Scaling:



Study Breakdown of Property (FA):Investigate how the breakdown of Property (FA) at higher ranks interacts with modular spectral transport and transcendentalism.



Goal: Develop a quantitative model showing that even if certain fixed-point properties (FA) break down, the inherent modularity still imposes rigid eigenvalue separation.



Develop Rank-Scaling Laws:Refine the relationship between the prime-based invariants (from P_G and Q_G) and the rank of elliptic curves or other algebraic structures.



Goal: Produce explicit formulas or bounds that predict rank limitations in terms of these modular invariants.







Algorithmic and Computational Directions:



Algorithm Design for Invariant Computation:Translate these abstract invariants into computational tools.



Approach: Use computational group theory (via systems like GAP or SageMath) to calculate character degrees, spectral gaps, and modular adjacency matrices.



Quantum Algorithm Applications:Leverage the connection between unitary representations and quantum algorithms (such as those solving the hidden subgroup problem) to estimate spectral gaps efficiently.



Goal: Develop new quantum algorithms that exploit modular spectral transport principles to compute invariants relevant to gauge theories.







Explore Broader Implications in Physics and Geometry




Interdisciplinary Workshops and Publications




Collaborative Research



TL;DR

The next steps involve a mix of deep theoretical work (refining categorical formulations and linking properties like (T) and (FA) to modular transport), algorithmic development (computing invariants and testing predictions), and exploring broad interdisciplinary implications—from geometric flows and entropy to quantum field theory and cryptographic complexity. This holistic approach will not only solidify the current framework but also extend it, revealing further unification among some of the most profound problems in modern mathematics and physics.