Project title: Probing the Higgs Potential with Graph Neural Networks
PI: Daniela Bortoletto
Measuring the Higgs boson’s properties is of great importance for particle physics. Its most crucial unobserved property is that it should interact with itself, which involves searching for the incredibly rare case of a single Higgs boson splitting into two Higgs bosons. Graph Neural Networks will be used for the first time to separate this small signal from a multitude of background processes, paving the way towards measuring the Higgs boson self-interaction.
