2008 (English)In: European Physical Journal C, ISSN 1434-6044, E-ISSN 1434-6052, Vol. 54, no 4, 617-644 p.Article, review/survey (Refereed) Published
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.
Place, publisher, year, edition, pages
2008. Vol. 54, no 4, 617-644 p.
IdentifiersURN: urn:nbn:se:kth:diva-85309DOI: 10.1140/epjc/s10052-008-0548-zISI: 000255415200009OAI: oai:DiVA.org:kth-85309DiVA: diva2:499757
QC 201202152012-02-132012-02-132012-02-15Bibliographically approved