Construction of a kinematic variable sensitive to the mass of the Standard Model Higgs boson in H → WW* → ℓ+νℓ -ν̄ using symbolic regression

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Abstract

We construct a kinematic variable that is sensitive to the mass of the Standard Model Higgs boson (MH) in the H → WW* → ℓ+νℓ-ν̄ channel using symbolic regression method. Explicit mass reconstruction is not possible in this channel due to the presence of two neutrinos that escape detection. Mass determination problem is that of finding a mass-sensitive function that depends on the measured observables. We use symbolic regression, which is a symbolic approach to the problem of non-linear regression, to derive an analytic formula sensitive to MH from the two lepton momenta and the missing transverse momentum. Using the newly-derived mass-sensitive variable, we expect Higgs mass resolutions between 1 to 4 GeV for MH between 130 and 190 GeV at the LHC with 10 fb?1 of data. This is the first time symbolic regression method has been applied to a particle physics problem.

Original languageEnglish
Article number110
JournalJournal of High Energy Physics
Volume2011
Issue number8
DOIs
Publication statusPublished - 2011 Sep 29

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Higgs bosons
regression analysis
kinematics
transverse momentum
escape
leptons
neutrinos
momentum
physics

Keywords

  • Supersymmetry phenomenology

ASJC Scopus subject areas

  • Nuclear and High Energy Physics

Cite this

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title = "Construction of a kinematic variable sensitive to the mass of the Standard Model Higgs boson in H → WW* → ℓ+νℓ -ν̄ using symbolic regression",
abstract = "We construct a kinematic variable that is sensitive to the mass of the Standard Model Higgs boson (MH) in the H → WW* → ℓ+νℓ-ν̄ channel using symbolic regression method. Explicit mass reconstruction is not possible in this channel due to the presence of two neutrinos that escape detection. Mass determination problem is that of finding a mass-sensitive function that depends on the measured observables. We use symbolic regression, which is a symbolic approach to the problem of non-linear regression, to derive an analytic formula sensitive to MH from the two lepton momenta and the missing transverse momentum. Using the newly-derived mass-sensitive variable, we expect Higgs mass resolutions between 1 to 4 GeV for MH between 130 and 190 GeV at the LHC with 10 fb?1 of data. This is the first time symbolic regression method has been applied to a particle physics problem.",
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N2 - We construct a kinematic variable that is sensitive to the mass of the Standard Model Higgs boson (MH) in the H → WW* → ℓ+νℓ-ν̄ channel using symbolic regression method. Explicit mass reconstruction is not possible in this channel due to the presence of two neutrinos that escape detection. Mass determination problem is that of finding a mass-sensitive function that depends on the measured observables. We use symbolic regression, which is a symbolic approach to the problem of non-linear regression, to derive an analytic formula sensitive to MH from the two lepton momenta and the missing transverse momentum. Using the newly-derived mass-sensitive variable, we expect Higgs mass resolutions between 1 to 4 GeV for MH between 130 and 190 GeV at the LHC with 10 fb?1 of data. This is the first time symbolic regression method has been applied to a particle physics problem.

AB - We construct a kinematic variable that is sensitive to the mass of the Standard Model Higgs boson (MH) in the H → WW* → ℓ+νℓ-ν̄ channel using symbolic regression method. Explicit mass reconstruction is not possible in this channel due to the presence of two neutrinos that escape detection. Mass determination problem is that of finding a mass-sensitive function that depends on the measured observables. We use symbolic regression, which is a symbolic approach to the problem of non-linear regression, to derive an analytic formula sensitive to MH from the two lepton momenta and the missing transverse momentum. Using the newly-derived mass-sensitive variable, we expect Higgs mass resolutions between 1 to 4 GeV for MH between 130 and 190 GeV at the LHC with 10 fb?1 of data. This is the first time symbolic regression method has been applied to a particle physics problem.

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