Speaker
Description
Multivariate classifiers in collider physics increasingly use low-level detector information to maximize sensitivity, but this often amplifies systematic uncertainties arising from mismodelling in simulation. To address this, we apply unsupervised domain adaptation (UDA), allowing the classifier to learn with both simulated and unlabeled real data during training, thereby reducing sensitivity to simulation-induced domain shifts. Our approach leverages a maximum mean discrepancy (MMD) penalty as a plug-in module for standard deep learning architectures. Using a case study of VBF versus ggF Higgs classification in H→γγ with both CNN and Particle Transformer architectures, we show that this method consistently improves classifier robustness and reduces cross section measurement uncertainty, demonstrating its universal effectiveness across data types.