Sharpening the A→Z(∗)h signature of the type-II 2HDM at the LHC through advanced machine learning

Abstract

The A→Z(∗)h decay signature has been highlighted as possibly being the first testable probe of the Standard Model (SM) Higgs boson discovered in 2012 (h) interacting with Higgs companion states, such as those existing in a 2-Higgs Doublet Model (2HDM), chiefly, a CP-odd one (A). The production mechanism of the latter at the Large Hadron Collider (LHC) takes place via bb¯-annihilation and/or gg-fusion, depending on the 2HDM parameters, in turn dictated by the Yukawa structure of this Beyond the SM (BSM) scenario. Among the possible incarnations of the 2HDM, we test here the so-called Type-II, for a twofold reason. On the one hand, it intriguingly offers two very distinct parameter regions compliant with the SM-like Higgs measurements, i.e., where the so-called `SM limit' of the 2HDM can be achieved. On the other hand, in both configurations, the AZh coupling is generally small, hence the signal is strongly polluted by backgrounds, so that the exploitation of Machine Learning (ML) techniques becomes extremely useful. Ours approach in this respect is a three-prong one. Firstly, we adjust ML models to analyze all possible High Energy Physics (HEP) data types, so as to maximize the amount of input information. Secondly, unlike most `black-box' ML approaches currently in use in the HEP community, we exploit a (linear) Centered Kernel Alignment (CKA) similarity metric to analyze the learned representations in the hidden layers, thereby enabling an interpretative element of our results. Thirdly, we emphasise that the proposed ML models are generic and can thus be adopted in other physics problems. Concerning the one at hand, by using such advanced ML implementations, we ultimately show that the sensitivity of LHC searches in the l+l−bb¯ (l=e,μ) final state can significantly be improved with respect to traditional cut-and-count analyses and/or, etc

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ORCID for S. Moretti: orcid.org/0000-0002-8601-7246

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