# Revisiting One-Zero and Two-Zero Neutrino Mass Textures in Light of Recent Oscillation and Cosmological Data

arXiv: https://arxiv.org/abs/2607.08384 · HTML: https://mlsanity.com/paper/2607.08384/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 65 references that are in the map.
- First posted on arXiv 9 Jul 2026.

## Builds on (most cited first, 9 of 9)

- [Exploring the flavor structure of quarks and leptons with reinforcement learning](https://mlsanity.com/paper/2304.14176/) (2023)
- [Reinforcement learning-based statistical search strategy for an axion model from flavor](https://mlsanity.com/paper/2409.10023/) (2024)
- [Truth, beauty, and goodness in grand unification: a machine learning approach](https://mlsanity.com/paper/2411.06718/) (2024)
- [Exploring the Truth and Beauty of Theory Landscapes with Machine Learning](https://mlsanity.com/paper/2401.11513/) (2024)
- [Exploring the flavor structure of leptons via diffusion models](https://mlsanity.com/paper/2503.21432/) (2025)
- [Towards AI-assisted Neutrino Flavor Theory Design](https://mlsanity.com/paper/2506.08080/) (2025)
- [Good flavor search in SU(5): a machine learning approach](https://mlsanity.com/paper/2511.08154/) (2025)
- [Diffusion-model approach to flavor models: A case study for S4^prime modular flavor model](https://mlsanity.com/paper/2504.00944/) (2025)
- [Leveraging Reinforcement Learning, Genetic Algorithms and Transformers for background determination in particle physics](https://mlsanity.com/paper/2509.14894/) (2025)

## Built on by (most cited first, 1 of 1)

- [Uncovering Hidden Leptonic Correlations with Flow Matching and Autoencoders](https://mlsanity.com/paper/2608.15042/) (2026)

## Abstract (arXiv)

We revisit one-zero and two-zero textures of the neutrino mass matrix under current experimental and cosmological constraints. We identify the phenomenologically viable texture structures using the latest results on neutrino oscillation parameters, the cosmological bound on the sum of neutrino masses, the kinematic bound on the effective electron-neutrino mass, and limits from neutrinoless double-beta decay. For two-zero textures, several structures are still allowed if only the CMB bound on the neutrino mass sum is imposed. Among them, the B-series textures show a characteristic prediction for the Dirac CP phase, with δrm CP lying around π/2 and 3π/2, and are within the reach of future neutrinoless double-beta decay searches. When the stronger CMB+BAO constraint is included, however, only the A-series textures remain viable. Therefore, we also analyze one-zero textures by using machine learning techniques, particularly flow matching. It turns out that some of the texture structures are already excluded by current data, while the allowed ones give distinct predictions for sumi mi, mnuerm eff, langle meerangle, and δrm CP. We further discuss how the one-zero texture structures can arise from non-invertible selection rules.