Exploring New Physics at the LHC: From Higgs Boson Insights to Innovative Applications of Machine Learning

• Daohan Wang (Institute of theoretical physics, CAS)
• Daohan Wang (HEPHY Vienna (ÖAW))

Room: HEPHY PSK Meeting Room This talk explores the forefront of particle physics research at the Large Hadron Collider (LHC), highlighting the interplay between the theoretical exploration of Higgs boson properties and the practical advancements enabled by machine learning. Initially, we touch upon the Type-I Two-Higgs-Doublet Model (2HDM), focusing on innovative strategies to probe electroweak signatures and the elusive fermiophobic Higgs bosons, setting the stage for uncovering new physics phenomena. We then transition to showcasing how machine learning, particularly through projects on probing light fermiophobic Higgs boson and triple Higgs boson coupling, revolutionizes our approach to these complex challenges. By integrating advanced algorithms, we enhance the precision and efficiency of detecting and analyzing Higgs-related events. The final segment of this talk emphasizes the application of machine learning in jet tagging, an essential task in particle physics experiments. We introduce two state-of-the-art models, the Dual Attention Transformer and the Hierarchical Energy Flow Network, which have significantly advanced our capability to identify and classify jet substructures. These innovations not only offer improved accuracy and efficiency in jet tagging but also demonstrate the potential of machine learning to reshape the landscape of high-energy physics research.