Revisiting One-Zero and Two-Zero Neutrino Mass Textures in Light of Recent Oscillation and Cosmological Data
This paper re-evaluates one-zero and two-zero neutrino mass textures against recent oscillation and cosmological data, finding that while several two-zero structures remain viable under CMB constraints alone, only the -series survives stricter CMB+BAO limits, and it further employs machine learning to analyze one-zero textures and their potential origin in non-invertible selection rules.
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Technical Summary: Revisiting One-Zero and Two-Zero Neutrino Mass Textures
Problem Statement
The Standard Model (SM) fails to account for non-zero neutrino masses, a fact established by neutrino oscillation experiments. While the dimension-five Weinberg operator provides an effective low-energy description of neutrino masses, the underlying structure of the neutrino mass matrix () remains unknown. A common theoretical approach to constrain this structure involves "texture zeros"—specific elements in or its inverse (minors) that vanish due to underlying symmetries.
While two-zero texture and two-zero minor structures have been extensively studied, recent high-precision data from neutrino oscillation experiments (NuFIT 6.0) and cosmological observations (Planck, ACT, DESI) have tightened constraints significantly. Many previously viable two-zero structures are now excluded or severely restricted. Furthermore, while one-zero textures (a single vanishing element) are theoretically motivated by non-invertible selection rules and symmetries, a comprehensive analysis of all one-zero textures and minors under current cosmological bounds (specifically the sum of neutrino masses, ) has not been performed.
Methodology
The authors employ a dual-methodology approach to analyze both two-zero and one-zero texture/minor structures:
Analytical Framework:
- The neutrino mass matrix in the flavor basis is diagonalized using the PMNS matrix ().
- For two-zero textures, the condition that two elements of vanish yields two complex equations. These are solved to express the Majorana phases () and mass ratios () as functions of the mixing angles () and the Dirac CP phase ().
- For one-zero textures, a single vanishing element leads to a complex equation. Due to the under-constrained nature of a single equation, the authors utilize the triangle inequality to derive bounds on the parameters.
- The analysis incorporates the latest global fit values for oscillation parameters (NuFIT 6.0) and applies constraints from:
- Cosmology: Planck CMB alone ( eV) and combined Planck+ACT+DESI BAO data ( eV for Normal Ordering, eV for Inverted Ordering).
- Kinematics: KATRIN limit on effective electron-neutrino mass ().
- Neutrinoless Double-Beta Decay (): Current limits on the effective Majorana mass ().
Machine Learning (Flow Matching):
- To explore the high-dimensional parameter space of one-zero textures efficiently, the authors utilize flow matching, a generative AI framework.
- The model is trained to learn the conditional distribution of mass matrix parameters () given observed physical labels (), such as mass-squared differences and PMNS matrix elements.
- The training data is generated by sampling complex mass matrix elements and diagonalizing them. The network is conditioned on experimental best-fit values.
- A fine-tuning procedure is applied to specific textures (e.g., G2 in Normal Ordering) to improve the accuracy of the generated viable parameter sets, minimizing a function defined by the deviation from experimental central values.
Key Contributions and Results
Two-Zero Textures:
- Under the CMB-only bound ( eV), eight two-zero textures remain viable (A1, A2, B1-B4 for Normal Ordering; B1, B3, C for Inverted Ordering).
- Under the stricter CMB+BAO bound, the viable set shrinks significantly. Only A1 and A2 textures remain viable for Normal Ordering. All two-zero textures are excluded for Inverted Ordering under these tight constraints.
- The B-series textures (viable under CMB-only) predict near and and relatively large , making them targets for future experiments like nEXO.
One-Zero Textures (via Flow Matching and Analytical Methods):
- Due to having more degrees of freedom, one-zero textures are less constrained than two-zero textures.
- Normal Ordering (NO): Under CMB+BAO constraints, textures G1, H1, and H2 remain viable. G2, G3, and H3 are excluded.
- Inverted Ordering (IO): Under CMB+BAO constraints, textures G2, G3, H1, and H2 remain viable. G1 and H3 are excluded.
- Predictions:
- Viable IO textures predict sizable , though generally lower than the two-zero IO cases.
- The H1 and H2 textures in IO show a characteristic preference for around and .
- The sum of neutrino masses () for viable one-zero textures is generally lower than in the two-zero case.
- The results from the flow matching analysis are confirmed to be in good agreement with the analytical method using triangle inequalities.
Two-Zero and One-Zero Minors:
- The analysis extends to minors (vanishing elements in ).
- Most two-zero minor structures are excluded under CMB+BAO constraints, with only D1 and D2 remaining viable for NO.
- One-zero minors are generally more robust, with most structures remaining viable under current cosmological bounds, though G1 (NO) and G2, G3, H3 (IO) are excluded.
Theoretical Realization:
- The paper discusses the realization of one-zero textures via non-invertible selection rules arising from gauging of groups (specifically and ).
- It is shown that standard symmetries cannot realize G1, G2, or G3 textures.
- However, using non-invertible fusion rules in Type II 2HDM or MSSM scenarios, the H1, H2, and H3 textures can be realized via gauging of .
- The G1, G2, and G3 textures can be realized via gauging of .
Significance
The paper provides a comprehensive update on neutrino mass textures in light of the most stringent cosmological limits available (DESI BAO + CMB). It demonstrates that while two-zero textures are becoming highly constrained (leaving only A1 and A2 for NO under tight bounds), one-zero textures offer a broader window of viability.
The primary significance lies in the application of flow matching to systematically scan the parameter space of one-zero textures, identifying viable regions and characteristic predictions for , , and that distinguish them from two-zero scenarios. Furthermore, the work connects these phenomenological classifications to a specific theoretical origin: non-invertible selection rules, providing a mechanism for how specific texture zeros (particularly the one-zero cases) could arise in fundamental physics beyond the Standard Model. The results suggest that future precision measurements of and , alongside next-generation searches, will be critical in discriminating between these texture scenarios.
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