A RealNVP normalizing flow trained inside nested sampling accelerates Bayesian scans of the Type-II seesaw parameter space and yields posterior constraints on scalar masses and couplings.
Resurrecting the LHC discovery potential in the extended Type-II Seesaw Model
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abstract
The juxtaposition of the precision of lepton flavour measurements and the limited energy range of the Large Hadron Collider (LHC) to discover dynamical degrees of freedom linked to the generation of the observed lepton mass patterns naively suggests only a limited relevance of the LHC's high luminosity phase. This, potentially, extends to future colliders. Using the concrete example of the type-II seesaw model and its effective field theory extension, we show that blind directions create a rich phenomenological interplay of muon precision measurements and electroweak resonance searches at present and future colliders, with testable implications for the HL-LHC phase.
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Normalizing Flow-Assisted Nested Sampling on Type-II Seesaw Model
A RealNVP normalizing flow trained inside nested sampling accelerates Bayesian scans of the Type-II seesaw parameter space and yields posterior constraints on scalar masses and couplings.