Speaker
Description
We consider a three-Higgs-doublet extension of the electroweak Standard Model invariant under a $Z_2 \times Z_2$ symmetry. Since necessary and sufficient bounded-from-below (BFB) conditions are not known for this model, we propose an approach based on increasingly stringent necessary conditions. This procedure is implemented in the Mathematica package StableWein, which allows the user to control the accuracy of the BFB analysis. Our results indicate that StableWein can identify BFB potentials with very high precision.
The package also includes a neural-network option trained to classify $Z_2 \times Z_2$-symmetric potentials. This machine-learning approach does not rely on either sufficient or necessary analytic BFB conditions, yet it achieves high performance, with an accuracy close to (99.9%) and fast inference. Therefore, the proposed method may be adapted to classify BFB parameter points in models for which analytic bounded-from-below conditions are unavailable.