Measuring Supersymmetry

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dc.creatorLafaye, Remi
dc.creatorPlehn, Tilman
dc.creatorRauch, Michael
dc.creatorZerwas, Dirk
dc.date2007-09-25
dc.date2008-01-28
dc.date.accessioned2026-07-07T11:37:30Z
dc.date.available2026-07-07T11:37:30Z
dc.descriptionIf 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.
dc.description43 pages, 55 figures, 13 tables; version to appear in EPJC
dc.identifierhttps://arxiv.org/abs/0709.3985
dc.identifierhttp://arxiv.org/abs/0709.3985
dc.identifierEur.Phys.J.C54:617-644,2008
dc.identifierdoi:10.1140/epjc/s10052-008-0548-z
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/199225
dc.subjectHigh Energy Physics - Phenomenology
dc.subjectHigh Energy Physics - Experiment
dc.titleMeasuring Supersymmetry
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