Deep-Learning in Search of Light Charged Higgs
arXiv:1803.01550
Abstract
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. Additionally, it is shown that increasing the number of processing units in DNNs does not necessarily cause an increase in efficiency due mainly to increased complexity. Our method and results can set an example of signal extraction from strong backgrounds.
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