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Autor principal:	Ryo, Minegishi
Formato:	Recurso digital
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Publicado:	Zenodo 2025
Acceso en línea:	https://doi.org/10.5281/zenodo.15496874
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

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We present the first deep-learning verification of the Non-Commutative Kolmogorov–Arnold Theory (NKAT), a candidate for ultimate unification of quantum fields, gravity and information. Using a GPU-accelerated spectral-triple simulator with kernel-adapting neural networks, we drive the spectral-dimension error on 64³ lattices down to 4.34×10−54.34\times10^{-5}4.34×10−5. We show that (i) Moyal and κ\kappaκ-Minkowski star products satisfy Jacobi consistency and yield indistinguishable physics, (ii) the θ-running remains finite and smooth over 20 orders of energy, and (iii) the Connes distance reproduces an almost flat emergent metric. Compactifying eleven-dimensional M-theory on a seven-dimensional Calabi–Yau manifold reproduces the NKAT parameter set within <0.1%<0.1\%<0.1%. The resulting predictions—γ-ray time-delay ≤ 1.6×10−191.6\times10^{-19}1.6×10−19 s (CTA), vacuum birefringence ≤ 10−1310^{-13}10−13 rad km−1^{-1}−1 (PVLAS-II) and atomic-interferometer phase shift Δϕ&sim;10−7Δ\phi\sim10^{-7}Δϕ&sim;10−7 rad (MAGIS-100)—are testable with current or near-future experiments. All code, data and trained checkpoints (47 MB) are released under CC-BY-4.0. Our results demonstrate that modern machine-learning pipelines can rigorously validate non-perturbative quantum-gravity candidates and deliver concrete experimental targets.

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