Abstract
Vector boson fusion proposed initially as an alternative channel for finding heavy Higgs has now established itself as a crucial search scheme to probe different properties of the Higgs boson or for new physics. We explore the merit of deep-learning entirely from the low-level calorimeter data in the search for invisibly decaying Higgs. Such an effort supersedes decades-old faith in the remarkable event kinematics and radiation pattern as a signature to the absence of any color exchange between incoming partons in the vector boson fusion mechanism. We investigate among different neural network architectures, considering both low-level and high-level input variables as a detailed comparative analysis. To have a consistent comparison with existing techniques, we closely follow a recent experimental study of CMS search on invisible Higgs with 36 fb^{-1} data. We find that sophisticated deep-learning techniques have the impressive capability to improve the bound on invisible branching ratio by a factor of three, utilizing the same amount of data. Without relying on any exclusive event reconstruction, this novel technique can provide the most stringent bounds on the invisible branching ratio of the SM-like Higgs boson. Such an outcome has the ability to constraint many different BSM models severely.
Highlights
Vector-boson fusion (VBF) production of color singlet particles provide a unique signature in hadron colliders
The VBF process was proposed as the most important mechanism for heavy Higgs searches [57] thanks to a much slower fall in cross-section compared to the s-channel mediated process
The difference is due to the SEW contribution since SQC D has a very similar shape as that of the background. This is another characteristic of VBF processes that the leading jets, originating from electroweak vertices, have lower separation in φ compared to those originating from QCD
Summary
Taking an analogy from jet-image classification, we use the full calorimeter image to study the invisible Higgs production in association with a pair of jets. This is a representative event from Z (νν) + jets background, where the jets arise from QCD vertices, which inherently has a much higher hadronic activity in the central regions between the two leading jets. Electroweak VBF production of Higgs can satisfy such criteria naturally with excellent efficiency These same criteria can ensure the elimination of vast QCD backgrounds up to a large extent, where jets are produced with a massive W or Z boson decaying (semi)invisibly. While the present study can be extended for other decay modes of Higgs, we choose the invisible channel for our study to showcase the importance of deep learning quantitatively using different neural network architectures. We close our discussion with the summary and conclusion in the last section
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