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.
Suzuki, Fragmentation of Hadrons from Heavy Quark Partons, Phys
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The authors generalize their prior spherical-source method to non-spherical sources with a self-consistent treatment of Coulomb interactions and provide software for three-dimensional correlation functions.
Fully heavy pentaquark fragmentation functions PQ5Q1.0 are completed for charm and bottom flavors and used to compute NLL/NLO+ semi-inclusive production rates at future colliders.
A model-independent parameterization of fermion mass matrices and PMNS matrix is constructed from masses and free parameters, with a chi-squared analysis showing consistency with experimental neutrino data.
citing papers explorer
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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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A self-consistent calculation of non-spherical Bose-Einstein correlation functions with Coulomb final-state interaction
The authors generalize their prior spherical-source method to non-spherical sources with a self-consistent treatment of Coulomb interactions and provide software for three-dimensional correlation functions.
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Fully Heavy Pentaquarks with JETHAD: A High-Energy Viewpoint
Fully heavy pentaquark fragmentation functions PQ5Q1.0 are completed for charm and bottom flavors and used to compute NLL/NLO+ semi-inclusive production rates at future colliders.
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Systematic analysis of fermionic masses and flavor mixings: a model-independent approach
A model-independent parameterization of fermion mass matrices and PMNS matrix is constructed from masses and free parameters, with a chi-squared analysis showing consistency with experimental neutrino data.