Topological Origin of 𝐽ₑff: From Spectral Determinant to Exploratory Closed Form

This paper demonstrates that \(J_{\mathrm{eff}}\) is not a free parameter requiring “fitting”, but a rigid reading of the \(\mathcal{T}^{64}\) topological structure on the lepton-mass cross-section. \(J_{\mathrm{eff}}\) is defined via the regularised spectral determinant of the Laplace–Beltrami operator restricted to the lepton homology classes \(\mathcal{H}_{12}\oplus\mathcal{H}_{24}\oplus\mathcal{H}_{36}\). Under dimensional-averaging and symmetry-allocation hypotheses an exploratory closed form is obtained:\[J_{\mathrm{eff}}^{\mathrm{(avg)}}=N^{1/64}\cdot\phi^{-35/64}=[\alpha^{2}(1-\alpha)]^{-1/64}\cdot\phi^{-35/64}\approx1.517,\]where \(N=[\alpha^{2}(1-\alpha)]^{-1}\) is the global normalisation factor. The complete spectral-zeta computation (exact projection onto the lepton homology classes followed by evaluation of \(\zeta'(0)\)) remains future work. After adoption of present spectral-weight estimates the lepton-mass readings agree with experiment (relative deviation \(<0.01\%\)). Current status: path identified / exploratory closed form locked; complete spectral computation pending. --- 本文证明 \(J_{\mathrm{eff}}\) 不是需要被“拟合”的自由参数,而是 \(\mathcal{T}^{64}\) 拓扑结构在轻子质量截面上的刚性读数。\(J_{\mathrm{eff}}\) 通过限制在轻子同调类 \(\mathcal{H}_{12}\oplus\mathcal{H}_{24}\oplus\mathcal{H}_{36}\) 上的拉普拉斯–贝尔特拉米算子的正则化谱行列式定义。在量纲平均与对称性分配假设下得到探索性闭式:\[J_{\mathrm{eff}}^{\mathrm{(平均)}}=N^{1/64}\cdot\phi^{-35/64}=[\alpha^{2}(1-\alpha)]^{-1/64}\cdot\phi^{-35/64}\approx1.517,\]其中 \(N=[\alpha^{2}(1-\alpha)]^{-1}\) 为全域归一化因子。完整的谱zeta计算(轻子同调类精确投影 + \(\zeta'(0)\) 求值)仍为后续工作。在采用当前谱权重估计值后,轻子质量读数与实验值一致(相对偏差 \(<0.01\%\))。当前状态:路径已识别 / 探索性闭式已锁定,完整谱计算待完成。

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-06
DOI
https://doi.org/10.5281/zenodo.22468817
Primary Topic
Neutrino Physics Research
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Topological Origin of 𝐽ₑff: From Spectral Determinant to Exploratory Closed Form

Zhenyuan Acharya
Zenodo (CERN European Organization for Nuclear Research)
Neutrino Physics Research
preprint

Topological Origin of 𝐽ₑff: From Spectral Determinant to Exploratory Closed Form

Zhenyuan Acharya
preprint en

Abstract

This paper demonstrates that \(J_{\mathrm{eff}}\) is not a free parameter requiring “fitting”, but a rigid reading of the \(\mathcal{T}^{64}\) topological structure on the lepton-mass cross-section. \(J_{\mathrm{eff}}\) is defined via the regularised spectral determinant of the Laplace–Beltrami operator restricted to the lepton homology classes \(\mathcal{H}_{12}\oplus\mathcal{H}_{24}\oplus\mathcal{H}_{36}\). Under dimensional-averaging and symmetry-allocation hypotheses an exploratory closed form is obtained:\[J_{\mathrm{eff}}^{\mathrm{(avg)}}=N^{1/64}\cdot\phi^{-35/64}=[\alpha^{2}(1-\alpha)]^{-1/64}\cdot\phi^{-35/64}\approx1.517,\]where \(N=[\alpha^{2}(1-\alpha)]^{-1}\) is the global normalisation factor. The complete spectral-zeta computation (exact projection onto the lepton homology classes followed by evaluation of \(\zeta'(0)\)) remains future work. After adoption of present spectral-weight estimates the lepton-mass readings agree with experiment (relative deviation \(<0.01\%\)). Current status: path identified / exploratory closed form locked; complete spectral computation pending. --- 本文证明 \(J_{\mathrm{eff}}\) 不是需要被“拟合”的自由参数,而是 \(\mathcal{T}^{64}\) 拓扑结构在轻子质量截面上的刚性读数。\(J_{\mathrm{eff}}\) 通过限制在轻子同调类 \(\mathcal{H}_{12}\oplus\mathcal{H}_{24}\oplus\mathcal{H}_{36}\) 上的拉普拉斯–贝尔特拉米算子的正则化谱行列式定义。在量纲平均与对称性分配假设下得到探索性闭式:\[J_{\mathrm{eff}}^{\mathrm{(平均)}}=N^{1/64}\cdot\phi^{-35/64}=[\alpha^{2}(1-\alpha)]^{-1/64}\cdot\phi^{-35/64}\approx1.517,\]其中 \(N=[\alpha^{2}(1-\alpha)]^{-1}\) 为全域归一化因子。完整的谱zeta计算(轻子同调类精确投影 + \(\zeta'(0)\) 求值)仍为后续工作。在采用当前谱权重估计值后,轻子质量读数与实验值一致(相对偏差 \(<0.01\%\))。当前状态:路径已识别 / 探索性闭式已锁定,完整谱计算待完成。

Zenodo (CERN European Organization for Nuclear Research)
Shanghai Institute of Geological Survey (CN)
Quality Education
Neutrino Physics Research
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.