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| Glavni avtor: | |
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| Format: | Recurso digital |
| Jezik: | angleščina |
| Izdano: |
Zenodo
2026
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| Teme: | |
| Online dostop: | https://doi.org/10.5281/zenodo.19180984 |
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Kazalo:
- <p dir="ltr">This technical report provides a comprehensive empirical evaluation of the <strong>Myo Min Aung Unified Theory (MUT) v7.27</strong> through 100 systematic postdiction tests. The theory operates on a minimalist framework, utilizing only two fundamental parameters: the proton-based constant f_{\mathrm{MCR}} and a single galactic length scale \lambda = 7.1\,\mathrm{kpc} derived from SPARC rotation curves.</p> <p dir="ltr"><strong>The report documents the theory’s performance across seven distinct physical domains:</strong></p> <ol> <li><strong>Nuclear Physics:</strong> Precise reproduction of atomic masses (from ^1H to $^{238}$U) and identification of magic number stability.</li> <li><strong>Atomic Constants:</strong> Consistency with the Rydberg constant, Bohr radius, and fine-structure constant.</li> <li><strong>Stellar Structure:</strong> Accurate postdiction of solar fusion efficiency, central temperature, and stellar luminosity-mass relations (L\propto M^{3.5}).</li> <li><strong>Galactic Dynamics:</strong> Successful fits for galactic rotation curves across a wide range of SPARC galaxies without invoking dark matter.</li> <li><strong>Cosmology:</strong> Derivation of \Omega_m, \Omega_b, n_s, and CMB acoustic peak positions.</li> <li><strong>Big Bang Nucleosynthesis (BBN):</strong> Prediction of primordial abundances (He, D, ^3He), while identifying the "Lithium Problem" as a remaining challenge.</li> <li><strong>Quantum Gravity:</strong> Addressing black hole singularity resolution and the information paradox.</li> </ol> <p dir="ltr">The results show an <strong>87% success rate</strong> (87 out of 100 tests) in matching observational data within uncertainties. Discrepancies regarding the Hubble tension and S_8 parameter are transparently reported, providing a clear roadmap for future theoretical refinements. This document serves as a rigorous benchmark for the predictive power of the MUT framework.</p>