Requirements on collider data to match the precision of WMAP on supersymmetric dark matter

TL;DR

This work addresses how collider measurements can be leveraged to predict the neutralino relic density, Ωh^2, at the precision levels of WMAP and PLANCK within the MSSM, analyzing both mSUGRA and a model-independent PmSUGRA approach. The authors develop a two-pronged framework—one tied to high-scale SUSY-breaking parameters and another to weak-scale MSSM parameters—utilizing SOFTSUSY for spectrum generation and micrOMEGAs for relic-density calculations, and they introduce an iterative sensitivity method to quantify the collider-data requirements needed to reproduce cosmological observations. They identify three cosmologically viable regions (co-annihilation, Higgs funnel, focus point) and quantify the critical experimental accuracies on masses, mass differences, and Higgs-sector parameters (e.g., M_A, Γ_A, μ) that drive the relic density, while accounting for theoretical uncertainties from scale dependence and higher-order corrections. The results provide concrete targets for collider programs to test standard cosmology via the neutralino relic density, highlighting especially the necessity of precise slepton mass differences, heavy-Higgs sector measurements, and the Higgsino content of the LSP. Overall, the study guides how to translate collider observables into robust relic-density predictions, supporting the use of collider data to probe the nature of dark matter and potential deviations from standard cosmology.

Abstract

If future colliders discover supersymmetric particles and probe their properties, one could predict the dark matter density of the Universe and would constrain cosmology with the help of precision data provided by WMAP and PLANCK. We investigate how well the relic density can be predicted in minimal supergravity (mSUGRA), with and without the assumption of mSUGRA when analysing data. We determine the parameters to which the relic density is most sensitive, and quantify the collider accuracy needed. Theoretical errors in the prediction are investigated in some detail.