SU(2)_F bosons in a U(2)_F flavor model induce unsuppressed flavor-violating couplings, with K to pi X and mu to e X constraining the breaking scale v_phi to 10^11-10^12 GeV for light bosons and other processes constraining heavier states.
Exploring the flavor structure of quarks and leptons with reinforcement learning
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
We propose a method to explore the flavor structure of quarks and leptons with reinforcement learning. As a concrete model, we utilize a basic value-based algorithm for models with $U(1)$ flavor symmetry. By training neural networks on the $U(1)$ charges of quarks and leptons, the agent finds 21 models to be consistent with experimentally measured masses and mixing angles of quarks and leptons. In particular, an intrinsic value of normal ordering tends to be larger than that of inverted ordering, and the normal ordering is well fitted with the current experimental data in contrast to the inverted ordering. A specific value of effective mass for the neutrinoless double beta decay and a sizable leptonic CP violation induced by an angular component of flavon field are predicted by autonomous behavior of the agent. Our finding results indicate that the reinforcement learning can be a new method for understanding the flavor structure.
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hep-ph 6representative citing papers
With CMB+BAO mass-sum bounds only A1/A2 two-zero textures survive; viable one-zero textures (via flow matching) predict distinct Σmi, ⟨mee⟩, and δCP patterns, realizable by non-invertible selection rules.
Applies diffusion models to generate 10,000 neutrino mass matrices consistent with oscillation parameters in a seesaw model, revealing non-trivial distributions in CP phases and 0νββ effective mass.
Machine learning optimization is applied to find parameters yielding neutrino mass matrices with target textures in BSM leptonic models.
Machine learning optimization of a generalized SU(5) parameter y finds y ≈ 0.8 produces the closest match to the original model while resolving the fermion mass discrepancy.
The authors use Adam optimization over a 33-dimensional parameter space in an SU(5) model with 45 and 45bar Higgs representations to search for Yukawa values that make the proton lifetime exceed the Super-Kamiokande bound of 5.9e33 years.
citing papers explorer
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Phenomenology of Non-Abelian Gauge and Goldstone Bosons in a U(2) Flavor Model
SU(2)_F bosons in a U(2)_F flavor model induce unsuppressed flavor-violating couplings, with K to pi X and mu to e X constraining the breaking scale v_phi to 10^11-10^12 GeV for light bosons and other processes constraining heavier states.
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Revisiting One-Zero and Two-Zero Neutrino Mass Textures in Light of Recent Oscillation and Cosmological Data
With CMB+BAO mass-sum bounds only A1/A2 two-zero textures survive; viable one-zero textures (via flow matching) predict distinct Σmi, ⟨mee⟩, and δCP patterns, realizable by non-invertible selection rules.
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Exploring the flavor structure of leptons via diffusion models
Applies diffusion models to generate 10,000 neutrino mass matrices consistent with oscillation parameters in a seesaw model, revealing non-trivial distributions in CP phases and 0νββ effective mass.
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Rolling Down the Leptonic BSM Landscape Using Machine Learning Techniques
Machine learning optimization is applied to find parameters yielding neutrino mass matrices with target textures in BSM leptonic models.
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Good flavor search in SU(5): a machine learning approach
Machine learning optimization of a generalized SU(5) parameter y finds y ≈ 0.8 produces the closest match to the original model while resolving the fermion mass discrepancy.
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Optimizing Yukawa couplings to suppress Dimension-five Proton Decay in $SU(5)$ GUT
The authors use Adam optimization over a 33-dimensional parameter space in an SU(5) model with 45 and 45bar Higgs representations to search for Yukawa values that make the proton lifetime exceed the Super-Kamiokande bound of 5.9e33 years.