Measurement of the Higgs Boson Couplings and CP Structure Using Tau Leptons at the LHC

Zanzi, D.

doi:10.1016/j.nuclphysbps.2017.03.057

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…1, but for a prototype background event originated from a Z (νν) + jets production, where the jets originate from QCD vertices decay like h 0 → W W [59], h 0 → τ τ [60]. Therefore, it also plays a vital role in determining anomalous coupling to vector boson [61,62] or the CP properties of the Higgs [63,64]. Its clean features make it the most sensitive channel for searching invisible decay of the Higgs boson [65] and in search for physics beyond the standard model [66][67][68].…”

Section: Introductionmentioning

confidence: 99%

Invisible Higgs search through vector boson fusion: a deep learning approach

et al. 2020

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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$$^{-1}$$ - 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.

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Section: Introductionmentioning

confidence: 99%

Invisible Higgs search through vector boson fusion: a deep learning approach

et al. 2020

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“…VBF process holds great importance to measure Higgs coupling with gauge bosons and fermions as it allows independent observations of Higgs decay like h 0 → W W [41], h 0 → τ τ [42]. Therefore, it also plays a vital role to determine anomalous coupling to vector boson [43] or the CP properties of the Higgs [44,45]. Its clean features make it the most sensitive channel for searching invisible decay of the Higgs boson.…”

Section: Introductionmentioning

confidence: 99%

Invisible Higgs search through Vector Boson Fusion: A deep learning approach

Ngairangbam,

Bhardwaj,

Konar

et al. 2020

Preprint

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Vector boson fusion, originally proposed as an alternative channel for finding heavy Higgs, has now established itself as a crucial search scheme to probe different properties of Higgs or for new physics. We explore the merit of deep-learning entirely from the low-level calorimeter data searching for invisibly decaying Higgs, as a choice to supersede decades-old faith on its salient underlying event structure produced in vector boson fusion. 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, utilising 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.

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Enhancing CP measurement of the Yukawa interactions of top-quark at e−e+ collider

2019

Physics Letters B

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Measurement of the Higgs Boson Couplings and CP Structure Using Tau Leptons at the LHC

Cited by 3 publications

References 19 publications

Invisible Higgs search through vector boson fusion: a deep learning approach

Invisible Higgs search through vector boson fusion: a deep learning approach

Invisible Higgs search through Vector Boson Fusion: A deep learning approach

Enhancing CP measurement of the Yukawa interactions of top-quark at e−e+ collider

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