AMBer applies reinforcement learning with physics feedback to automate construction of neutrino flavor models that minimize free parameters, validated on known cases and extended to a new symmetry group.
Modular invariance faces precision neutrino data
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abstract
We analyze a modular invariant model of lepton masses, with neutrino masses originating either from the Weinberg operator or from the seesaw. The constraint provided by modular invariance is so strong that neutrino mass ratios, lepton mixing angles and Dirac/Majorana phases do not depend on any Lagrangian parameter. They only depend on the vacuum of the theory, parametrized in terms of a complex modulus and a real field. Thus eight measurable quantities are described by the three vacuum parameters, whose optimization provides an excellent fit to data for the Weinberg operator and a good fit for the seesaw case. Neutrino masses from the Weinberg operator (seesaw) have inverted (normal) ordering. Several sources of potential corrections, such as higher dimensional operators, renormalization group evolution and supersymmetry breaking effects, are carefully discussed and shown not to affect the predictions under reasonable conditions.
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A modular A4 flavor model with universal couplings reproduces charged lepton masses via the modulus tau and predicts correlated neutrino observables for normal mass ordering and right-handed weight k_N = -1.
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Towards AI-assisted Neutrino Flavor Theory Design
AMBer applies reinforcement learning with physics feedback to automate construction of neutrino flavor models that minimize free parameters, validated on known cases and extended to a new symmetry group.
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Lepton masses and mixing in non-holomorphic modular $A_4$ with universal couplings
A modular A4 flavor model with universal couplings reproduces charged lepton masses via the modulus tau and predicts correlated neutrino observables for normal mass ordering and right-handed weight k_N = -1.