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Discovery Potential for the SM Higgs Boson in the H -> WW* -> 2l2nu channel at LHC
High Energy Physics - Phenomenology · Submitted 8 Oct 2008
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Sets of sequential cuts are applied to each of the three topologies, in order to isolate a signal which exceeds the tt and continuum WW backgrounds.
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The abstract, sentence by sentence
A prospective analysis for the search of the Standard Model (SM) Higgs boson with the CMS detector is presented in the context of the early LHC data. The aim is to establish an analysis strategy for inclusive production of the Higgs boson decaying in WW* pairs in the context of the early LHC data. Higgs mass region between 120-200 GeV, in which this signature was proposed as highly sensitive, has been studied. The W decays into lnu are considered, where l stands for e or mu. The final states are characterized by two, opposite-sign, high transverse momentum leptons, missing energy, carried out by the undetected neutrinos, and little jet activity. This study uses Monte Carlo (MC) events with full detector simulation, including limited calibration and alignment precision as expected at the LHC startup. Sets of sequential cuts are applied to each of the three topologies, in order to isolate a signal which exceeds the tt and continuum WW backgrounds. Alternatively, an artificial neural network multi-variate analysis technique is used.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | A prospective analysis for the search of the Standard Model (SM) Higgs boson with the CMS detector is presented in the context of the early LHC data. | contribution 0.96 | 0.77 | 0.04 | asserted 0.83 |
| s1 | The aim is to establish an analysis strategy for inclusive production of the Higgs boson decaying in WW* pairs in the context of the early LHC data. | contribution 0.86 | 0.40 | 0.05 | asserted 0.86 |
| s2 | Higgs mass region between 120-200 GeV, in which this signature was proposed as highly sensitive, has been studied. | contribution 0.56 | 0.60 | 0.04 | asserted 0.92 |
| s3 | The W decays into lnu are considered, where l stands for e or mu. | method 0.84 | 0.21 | 0.02 | asserted 0.96 |
| s4 | The final states are characterized by two, opposite-sign, high transverse momentum leptons, missing energy, carried out by the undetected neutrinos, and little jet activity. | method 0.84 | 0.29 | 0.04 | asserted 0.99 |
| s5 | This study uses Monte Carlo (MC) events with full detector simulation, including limited calibration and alignment precision as expected at the LHC startup. | method 0.99 | 0.81 | 0.02 | asserted 0.99 |
| s6 | Sets of sequential cuts are applied to each of the three topologies, in order to isolate a signal which exceeds the tt and continuum WW backgrounds. | method 1.00 | 0.83 | 0.07 | asserted 0.99 |
| s7 | Alternatively, an artificial neural network multi-variate analysis technique is used. | method 0.98 | 0.76 | 0.04 | asserted 0.94 |
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Original abstract
A prospective analysis for the search of the Standard Model (SM) Higgs boson with the CMS detector is presented in the context of the early LHC data. The aim is to establish an analysis strategy for inclusive production of the Higgs boson decaying in WW* pairs in the context of the early LHC data. Higgs mass region between 120-200 GeV, in which this signature was proposed as highly sensitive, has been studied. The W decays into lnu are considered, where l stands for e or mu. The final states are characterized by two, opposite-sign, high transverse momentum leptons, missing energy, carried out by the undetected neutrinos, and little jet activity. This study uses Monte Carlo (MC) events with full detector simulation, including limited calibration and alignment precision as expected at the LHC startup. Sets of sequential cuts are applied to each of the three topologies, in order to isolate a signal which exceeds the tt and continuum WW backgrounds. Alternatively, an artificial neural network multi-variate analysis technique is used.
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