Speaker
Description
Using two benchmark models containing extended scalar sectors beyond the Standard Model, we investigate deep learning techniques to enhance the sensitivity of resonant triple Higgs boson ($HHH$) searches in the fully hadronic $6b$ channel, which suffers from severe combinatorial background and jet-pairing ambiguities. Specifically, we employ the Symmetry Preserving Attention Network (SPA-Net), a Transformer-based architecture that explicitly respects the permutational symmetries inherent in jet assignment. By performing multi-task learning to simultaneously tackle jet pairing and event classification directly from low-level jet features, SPA-Net eliminates the need for explicit jet permutation enumeration while building expressive latent event representations. Compared with conventional Dense Neural Networks, SPA-Net yields up to 40% more stringent limits on resonant production cross-sections. These results highlight the potential of symmetry-preserving deep learning models to overcome combinatorial barriers in high-multiplicity hadronic searches.