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A Feasibility Study on Measuring a Strange Sea Asymmetry in the Proton at ATLAS
The findings of a study into the feasibility of placing experimental constraints on a hypothetical asymmetry in the strange component of the proton sea, and on the strange components as a whole, at ATLAS are presented here. The strange and antistrange components of the proton sea (collectively known...
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Lenguaje: | eng |
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University of Oxford
2008
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Acceso en línea: | http://cds.cern.ch/record/1190745 |
Sumario: | The findings of a study into the feasibility of placing experimental constraints on a hypothetical asymmetry in the strange component of the proton sea, and on the strange components as a whole, at ATLAS are presented here. The strange and antistrange components of the proton sea (collectively known as the "strange sea") are generally assumed to be charge-symmetric, however the possibility of an asymmetric strange sea is indicated in theory and experiment, making this an interesting area for study in data. The strange component of the hadron sea at low values of x is not well constrained in data, and a measurement placing stronger limits on this will be vital in enabling Standard Model and beyond-Standard Model physics to be distinguished at the LHC. This Monte Carlo study addresses the question of whether the current limits on the strange sea could be improved using the ATLAS experiment, and whether if an asymmetry exists in the strange sea it is likely to be detectable at ATLAS. This study was performed using approximately 1 fb1 of simulated data. It was found that it will not be possible to measure properties of the strange sea in one inverse femtobarn of data. It is indicated that new limits on the strange component of the sea, and a possible measurement of a strange sea asymmetry, are feasible with around 300 inverse femtobarns of data provided that the levels of certain background channels with large cross-sections (n amely cc and bb backgrounds) could be convincingly shown to be controlled in further, higher-volume studies with data and Monte Carlo. |
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