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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
hep-ph 2verdicts
UNVERDICTED 2representative citing papers
In the minimal pseudo-Dirac HNL scenario, light-neutrino oscillation data fixes an ellipse in the active flavour simplex for leading active-heavy interactions via a single complex amplitude and Majorana phase.
citing papers explorer
-
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.
-
Neutrino oscillation data and a pseudo-Dirac heavy neutral lepton
In the minimal pseudo-Dirac HNL scenario, light-neutrino oscillation data fixes an ellipse in the active flavour simplex for leading active-heavy interactions via a single complex amplitude and Majorana phase.