Author
Title
Modeling the Predictions of Supersymmetry at the LHC using Bayesian Neural Networks
Abstract
There exist many unanswered questions in the area of High Energy Physics (HEP), even with the recent discovery of the Higgs boson. What is dark matter? Why is the Higgs mass so low? The currently accepted theory, the Standard Model (SM), provides no answers to these questions. Therefore, physicists are working on theories beyond the SM that may provide answers. One well-motivated class of theories is based on supersymmetry (SUSY), which associates each fundamental particle in the SM with a supersymmetric counterpart. A key prediction of SUSY is that supersymmetric particles, known as “sparticles”, will be created in pairs in high energy collisions, for example, gluino pairs as illustrated in the Feynman diagrams below. If these SUSY theories are viable, their predictions must be consistent with the observations being made at the Large Hadron Collider (LHC).
