LHC signals of a heavy doublet Higgs as dark matter portal: cut-based approach and improvement with gradient boosting and neural networks

Dey, Atri ; Lahiri, Jayita ; Mukhopadhyaya, Biswarup (2019) LHC signals of a heavy doublet Higgs as dark matter portal: cut-based approach and improvement with gradient boosting and neural networks Journal of High Energy Physics, 2019 (9). ISSN 1029-8479

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Official URL: http://doi.org/10.1007/JHEP09(2019)004

Related URL: http://dx.doi.org/10.1007/JHEP09(2019)004

Abstract

Though the 125-GeV scalar, as the Higgs boson of the standard model, is disfavoured as a dark matter portal by direct searches and the observations on relic density, a heavier scalar in an extended electroweak sector can fit into that role. We explore this possibility in the context of two Higgs doublet models (2HDM). Taking Type I and Type II 2HDM as illustration, and assuming a scalar gauge singlet dark matter particle, we show that the heavy neutral CP-even scalar (H) can (a) serve as dark matter portal consistently with all data, and (b) have a substantial invisible branching ratio, over a wide region of the parameter space. Using this fact, we estimate rates of LHC signals where H is produced via (i) gluon fusion, in association with a hard jet, and (ii) vector boson fusion. Invisible decays of the H can then lead to monojet + E/T in (i), and two forward jets with large rapidity gap + E/T in (ii). The second kind of signal usually yields better significance for the high-luminosity run. We also supplement our cut-based analyses with those based on gradient boosted decision trees (XGboost) and artificial neural network (ANN) techniques, where the statistical significance distinctly improves.

Item Type:Article
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ID Code:125411
Deposited On:04 Feb 2022 09:00
Last Modified:04 Feb 2022 09:00

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