Mitigation of the LHC Inverse Problem

TL;DR

This paper investigates the LHC inverse problem, demonstrating that most parameter degeneracies in supersymmetric models can be resolved using counting observables and systematic error reduction, aiding in parameter determination.

Contribution

It provides a detailed analysis showing how to distinguish degenerate supersymmetric parameter sets using  distributions and counting observables, improving parameter resolution methods.

Findings

Resolved 254 of 283 degenerate pairs without background.

Resolved 237 of 283 pairs when including Standard Model background.

Systematic error reduction can further eliminate residual degeneracies.

Abstract

The LHC inverse problem refers to the difficulties in determining the parameters of an underlying theory from data (to be) taken by the LHC experiments: if they find signals of new physics, and an underlying theory is assumed, could its parameters be determined uniquely, or do different parameter choices give indistinguishable experimental signatures? This inverse problem was studied before for a supersymmetric Standard Model with 15 free parameters. This earlier study found 283 indistinguishable pairs of parameter choices, called degenerate pairs, even if backgrounds are ignored. We can resolve all but 23 of those pairs by constructing a true \chi^2 distribution using mostly counting observables. The elimination of systematic errors would even allow separating the residual degeneracies. Taking the Standard Model background into account we still can resolve 237 of the 283 "degenerate" pairs. This indicates that (some of) our observables should also be useful for the purpose of determining the values of SUSY parameters.