Abstract
We study the tagging of Higgs exotic decay signals using different types of deep neural networks (DNNs), focusing on the $W^\pm h$ associated production channel followed by Higgs decaying into $n$ $b$-quarks with $n=4$, 6 and 8. All the Higgs decay products are collected into a fat-jet, to which we apply further selection using the DNNs. Three kinds of DNNs are considered, namely convolutional neural network (CNN), recursive neural network (RecNN) and particle flow network (PFN). The PFN can achieve the best performance because its structure allows enfolding more information in addition to the four-momentums of the jet constituents, such as particle ID and tracks parameters. Using the PFN as an example, we verify that it can serve as an efficient tagger even though it is trained on a different event topology with different $b$-multiplicity from the actual signal. The projected sensitivity to the branching ratio of Higgs decaying into $n$ $b$-quarks at the HL-LHC are 10\%, 3\% and 1\%, for $n=4$, 6 and 8, respectively.
Highlights
Higgs exotic decay is a promising window for probing new physics
In this article we study the possibility of probing the Higgs exotic decays via deep learning methods
We focus on the WÆh associated production with Higgs decaying to 4b, 6b, or 8b final states
Summary
Higgs exotic decay is a promising window for probing new physics. The Higgs portal can provide the most relevant coupling between the Higgs boson and new physics, while its narrow Standard Model width enhances the sensitivity to exotic decay modes. We consider a dark sector consisting of multiple dark scalars [4,5,6,7,8,9,10,11,12,13,14,15] Owing to their mixing with the Higgs boson, the final decay products would be heavy fermions, such as b jets. There could be cascade decays among the dark scalars, resulting in a variety of final states with different b multiplicities. As such, this furnishes a good example in.
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