Thesis

In the Standard Model (SM), the Higgs field breaks the electroweak symmetry, thereby giving mass to the W and Z bosons. The self-coupling of the Higgs boson remains unmeasured, yet it is crucial for understanding the shape of the Higgs potential and the stability of the universe’s vacuum. Studying Higgs pair production (di-Higgs) is the only direct way to access this parameter, providing key insights into the electroweak phase transition after the Big Bang. Di-Higgs production is extremely rare (cross-section ~40 fb for proton-proton collisions at a centre-of-mass energy of 13.6 TeV), and among its possible final states, the multilepton channel (channel with light leptons) is promising due to its distinctive kinematics, though complex due to diverse topologies and backgrounds. Recent advances in artificial intelligence, particularly transformer-based architectures respecting physical symmetries, have recently significantly improved event reconstruction in complex Higgs channels such as HH?4b or HH?bbtt. Applying these techniques to the multilepton channel offers strong potential to enhance sensitivity. This PhD project will focus on searching for di-Higgs production in the multilepton final state with the full ATLAS Run 3 dataset at 13.6 TeV, leveraging the group’s ongoing ttH multilepton work to develop advanced AI-based reconstruction and analysis methods. The projet aims to approach SM sensitivity for the Higgs self-coupling.