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We study the benefits of jet- and event-level deep learning methods indistinguishing vector boson fusion (VBF) from gluon-gluon fusion (GGF) Higgsproduction at the LHC. We show that a variety of classifiers (CNNs,attention-based networks) trained on the complete low-level inputs of the fullevent achieve significant performance gains over shallow machine learningmethods (BDTs) trained on jet kinematics and jet shapes, and we elucidate thereasons for these performance gains. Finally, we take initial steps towards thepossibility of a VBF vs. GGF tagger that is agnostic to the Higgs decay mode,by demonstrating that the performance of our event-level CNN does not changewhen the Higgs decay products are removed. These results highlight thepotentially powerful benefits of event-level deep learning at the LHC.

VBF vs. GGF Higgs with Full-Event Deep Learning: Towards a Decay-Agnostic Tagger