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arXiv · 1303.5386

Naturalness of Light Neutralino Dark Matter in pMSSM after LHC, XENON100 and Planck Data

Abstract

We examine the possibility of a light (below 46 GeV) neutralino dark matter (DM) candidate within the 19-parameter phenomenological Minimal Supersymmetric Standard Model (pMSSM) in the light of various recent experimental results, especially from the LHC, XENON100, and Planck. We also study the extent of electroweak fine-tuning for such a light neutralino scenario in view of the null results from the searches for supersymmetry so far. Using a Markov Chain Monte Carlo likelihood analysis of the full pMSSM parameter space, we find that a neutralino DM with mass > 10 GeV can in principle still satisfy all the existing constraints. Our light neutralino solutions can be broadly divided into two regions: (i) The solutions in the 10 - 30 GeV neutralino mass range are highly fine-tuned and require the existence of light selectrons (below 100 GeV) in order to satisfy the observed DM relic density. We note that these are not yet conclusively ruled out by the existing LEP/LHC results, and a dedicated analysis valid for a non-unified gaugino mass spectrum is required to exclude this possibility. (ii) The solutions with low fine-tuning are mainly in the 30 - 46 GeV neutralino mass range. However, a major portion of it is already ruled out by the latest XENON100 upper limits on its spin-independent direct detection cross section, and the rest of the allowed points are within the XENON1T projected limit. Thus, we show that the allowed MSSM parameter space for a light neutralino DM below the LEP limit of 46 GeV, possible in supersymmetric models without gaugino mass unification, could be completely accessible in near future. This might be useful in view of the recent claims for positive hints of a DM signal in some direct detection experiments.

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BibTeXRIS

Celine Boehm, P. S. Bhupal Dev, Anupam Mazumdar, Ernestas Pukartas. 2013-06-06. Naturalness of Light Neutralino Dark Matter in pMSSM after LHC, XENON100 and Planck Data. https://doi.org/10.1007/jhep06(2013)113

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