Efficient reconstruction of CMSSM parameters from LHC data - A case study

TL;DR

This paper introduces an efficient method for reconstructing CMSSM parameters from LHC data and cosmological information, demonstrating high accuracy and robustness across different priors and data scenarios.

Contribution

The authors develop a simple Gaussian approximation method for parameter reconstruction that is computationally efficient and applicable to various supersymmetric models, incorporating external data effectively.

Findings

Gaussian approximation accurately recovers model parameters

Results are consistent across different priors

Additional cosmological data improves parameter constraints

Abstract

We present an efficient method of reconstructing the parameters of the Constrained MSSM from assumed future LHC data, applied both on their own right and in combination with the cosmological determination of the relic dark matter abundance. Focusing on the ATLAS SU3 benchmark point, we demonstrate that our simple Gaussian approximation can recover the values of its parameters remarkably well. We examine two popular non-informative priors and obtain very similar results, although when we use an informative, naturalness-motivated prior, we find some sizeable differences. We show that a further strong improvement in reconstructing the SU3 parameters can by achieved by applying additional information about the relic abundance at the level of WMAP accuracy, although the expected data from Planck will have only a very limited additional impact. Further external data may be required to break some remaining degeneracies. We argue that the method presented here is applicable to a wide class of low-energy effective supersymmetric models, as it does not require to deal with purely experimental issues, eg, detector performance, and has the additional advantages of computational efficiency. Furthermore, our approach allows one to distinguish the effect of the model's internal structure and of the external data on the final parameters constraints.