Higgs Production Classifier using Weak Supervision

TL;DR

This paper introduces a weak supervision approach using deep neural networks to classify Higgs production mechanisms in collider data, reducing reliance on simulations and demonstrating transferability across decay modes.

Contribution

It applies weak supervision with CWoLa to train neural networks on real collider data, comparing CNN and transformer models, and shows improved performance with data augmentation and decay mode agnosticism.

Findings

Performance slightly improves when photon info is removed in low-luminosity regions.

Data augmentation with physics-motivated methods enhances classifier accuracy.

Models trained on diphoton data successfully classify other Higgs decay modes.

Abstract

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.