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arXiv 0709.3985hep-phhep-ex
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测量超对称性

Measuring Supersymmetry

Remi Lafaye, Tilman Plehn, Michael Rauch, Dirk Zerwas

摘要:

如果在大型强子对撞机(和ILC)中发现了新的物理学,那么基础理论的重建不应该受到关于高尺度模型的假设的影响。对于许多测量到高维参数空间的映射,我们介绍了SFitter及其新的加权马尔可夫链技术。SFitter构造一个唯一的似然图,确定最佳拟合参数点,并产生一个最可能的参数点的排名列表。使用的例子TeV-规模的超对称拉格朗日,我们展示了如何高维似然图一般包括退化和强相关性。SFitter使我们能够研究这样的模型-参数空间采用贝叶斯以及frequentist建设。我们详细说明它应该是如何可能分析高维新的物理参数空间,如TeV-规模MSSM在LHC。LHC和ILC测量的组合可能完全能够覆盖高度复杂的TeV尺度参数空间。

英文摘要:

If new physics is found at the LHC (and the ILC) the reconstruction of the underlying theory should not be biased by assumptions about high--scale models. For the mapping of many measurements onto high--dimensional parameter spaces we introduce SFitter with its new weighted Markov chain technique. SFitter constructs an exclusive likelihood map, determines the best--fitting parameter point and produces a ranked list of the most likely parameter points. Using the example of the TeV--scale supersymmetric Lagrangian we show how a high--dimensional likelihood map will generally include degeneracies and strong correlations. SFitter allows us to study such model--parameter spaces employing Bayesian as well as frequentist constructions. We illustrate in detail how it should be possible to analyze high--dimensional new--physics parameter spaces like the TeV--scale MSSM at the LHC. A combination of LHC and ILC measurements might well be able to completely cover highly complex TeV--scale parameter spaces.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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