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.
Multifield dynamics of supersymmetric Higgs inflation in SU(5) GUT
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abstract
We study the Higgs inflation model realized in the supersymmetric SU(5) grand unified theory (GUT), focusing on its multifield dynamics and prediction of cosmological observables. The requirement for GUT symmetry breaking during inflation imposes tight constraints on the model parameters. We find, nevertheless, with an appropriately chosen noncanonical Kahler potential the model is in excellent agreement with the present cosmological observation. The effects from multifield dynamics is found to be minor and thus, unlike other similar supersymmetric implementation of nonminimally coupled Higgs inflation, the prediction of this model is robust against multifield ambiguities.
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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.