Measuring Supersymmetry

TL;DR

This paper introduces SFitter, a new method using weighted Markov chains to analyze high-dimensional supersymmetric models at the LHC and ILC, enabling unbiased reconstruction of underlying theories.

Contribution

The paper presents SFitter, a novel tool for mapping measurements onto high-dimensional parameter spaces with Bayesian and frequentist approaches, handling degeneracies and correlations.

Findings

SFitter constructs detailed likelihood maps for supersymmetric models.

It can identify best-fit parameters and their degeneracies.

Combining LHC and ILC data enhances coverage of complex parameter spaces.

Abstract

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.