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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 AA-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.

Original authors: Haruto Kitagawa, Coh Miyao, Satsuki Nishimura, Hajime Otsuka

Published 2026-07-10
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Original authors: Haruto Kitagawa, Coh Miyao, Satsuki Nishimura, Hajime Otsuka

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

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 (MνM_\nu) remains unknown. A common theoretical approach to constrain this structure involves "texture zeros"—specific elements in MνM_\nu 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 U(1)LμLτU(1)_{L_\mu - L_\tau} symmetries, a comprehensive analysis of all one-zero textures and minors under current cosmological bounds (specifically the sum of neutrino masses, mν\sum m_\nu) has not been performed.

Methodology
The authors employ a dual-methodology approach to analyze both two-zero and one-zero texture/minor structures:

  1. Analytical Framework:

    • The neutrino mass matrix in the flavor basis is diagonalized using the PMNS matrix (UPMNSU_{PMNS}).
    • For two-zero textures, the condition that two elements of MνM_\nu vanish yields two complex equations. These are solved to express the Majorana phases (α2,α3\alpha_2, \alpha_3) and mass ratios (m2/m1,m3/m1m_2/m_1, m_3/m_1) as functions of the mixing angles (θij\theta_{ij}) and the Dirac CP phase (δCP\delta_{CP}).
    • 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 (mν<0.21\sum m_\nu < 0.21 eV) and combined Planck+ACT+DESI BAO data (mν<0.113\sum m_\nu < 0.113 eV for Normal Ordering, <0.145< 0.145 eV for Inverted Ordering).
      • Kinematics: KATRIN limit on effective electron-neutrino mass (mνeeffm_{\nu_e}^{eff}).
      • Neutrinoless Double-Beta Decay (0νββ0\nu\beta\beta): Current limits on the effective Majorana mass (mee\langle m_{ee} \rangle).
  2. 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 (GG) given observed physical labels (LL), 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 χ2\chi^2 function defined by the deviation from experimental central values.

Key Contributions and Results

  • Two-Zero Textures:

    • Under the CMB-only bound (mν<0.21\sum m_\nu < 0.21 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 δCP\delta_{CP} near π/2\pi/2 and 3π/23\pi/2 and relatively large mee\langle m_{ee} \rangle, making them targets for future 0νββ0\nu\beta\beta 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 mee\langle m_{ee} \rangle, though generally lower than the two-zero IO cases.
      • The H1 and H2 textures in IO show a characteristic preference for δCP\delta_{CP} around π/2\pi/2 and 3π/23\pi/2.
      • The sum of neutrino masses (mi\sum m_i) 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 Mν1M_\nu^{-1}).
    • 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 Z2\mathbb{Z}_2 gauging of ZN\mathbb{Z}_N groups (specifically Z5\mathbb{Z}_5 and Z7\mathbb{Z}_7).
    • It is shown that standard U(1)U(1) 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 Z2\mathbb{Z}_2 gauging of Z5\mathbb{Z}_5.
    • The G1, G2, and G3 textures can be realized via Z3\mathbb{Z}_3 gauging of Z7\mathbb{Z}_7.

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 δCP\delta_{CP}, mi\sum m_i, and mee\langle m_{ee} \rangle 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 δCP\delta_{CP} and mν\sum m_\nu, alongside next-generation 0νββ0\nu\beta\beta searches, will be critical in discriminating between these texture scenarios.

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