Higgs Production Classifier using Weak Supervision

A reliable determination of the Higgs production mechanism in hadron collider experiments is essential in the program of the measurements of the Higgs couplings. We employ weak supervision, CWoLa in particular, to train deep neural networks using real data of the diphoton events, in the hope of reducing biases resulting from Monte Carlo simulations. Models based on the convolutional neural network and the transformer are tested and compared. In particular, the classification performance gets slightly better when the photon information is removed from training on the low-luminosity region of $H \to \gamma \gamma$. We explicitly show that the performance can be improved when the training dataset is enlarged by data augmentation using physics-motivated methods. We further demonstrate that the trained model can be successfully applied to the $H \to ZZ$ and $H \to Z\gamma$ events, showing that such classifiers are agnostic to Higgs decay modes provided they do not involve strong QCD corrections.