  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 Multi-Layer 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 a mono-type data
as input, frittering away invaluable multi-source and multi-scale 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 MultiLayers Perceptron or Convolution Neural Network, the Hybrid
Deep Neural Networks provide higher capacity in feature extraction and thus in
signal vs background classification performance. We provide reference results
for the case of the High-Luminosity Large Hadron Collider.
