Deep-Learning in Search of Light Charged Higgs

Understand

In this work, we deep-learn light charged Higgs signal in top quark decays which poses difficulties due to strong W boson contamination.

• We construct Deep Neural Networks (DNN) with appropriate architecture and determine signal extraction efficiency by considering various features (kinematical and human engineered parameters).
• Results show that DNN gives better performance than the classical neural networks and has ability to find regions of high efficiency even the input features are not human-engineered.
• In a sense, human-engineered high-level features are offset by DNNs with different combinations of the low-level kinematical features.