Search for mono-Higgs signals in bb¯ final states using deep neural networks

Abstract

We study mono-Higgs signatures emerging in an illustrative new physics scenario involving Standard Model Higgs boson decays to bottom quark pairs using hybrid deep neural networks. We use a multilayer perceptron to analyze the kinematic observables and optimize the signal-to-background discrimination. The global color flow structure of hard jets emerging from the decay of heavy particles with different color charges is crucial to single out the mono-Higgs signature. Upon embedding the different color flow structures for signal and backgrounds into constructed images, we use a convolution neural network to analyze the latter. Specifically, the approach takes initially mono-type data as input, frittering away invaluable multisource and multiscale information. We then discuss a general architecture of hybrid deep neural networks that supports instead mixed input data. In comparison with single input deep neural networks, like multilayer perceptron or convolution neural networks, the hybrid deep neural networks provide higher capacity in feature extraction and thus in signal versus background classification performance. We provide reference results for the case of the high-luminosity Large Hadron Collider.