Speaker
Description
Understanding the higgs boson, both in the context of Standard Model physics and beyond-the-Standard Model hypotheses, is a key problem in modern particle physics. An increased understanding could come from the detection and analysis of pairs of higgs bosons produced at hadron colliders. While such higgs pairs have not yet been observed at the Large Hadron Collider (LHC), it is likely that they will be detected within the next few years at the High-Luminosity LHC. In this study, we show how a machine-learning based higgs pair analysis can constrain several dimension-6 SMEFT Wilson coefficients in the higgs sector. We find that including shape-level information, e.g. in the form of the distributions of kinematic observables, in such analyses is likely to place tighter constraints on the coefficients than a rate-only analysis.
