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Dark matter signals at the LHC: forecasts from ton-scale direct detection experiments

2012/11/21 by Giorgio Arcadi, Arcadi, Giorgio, Riccardo Catena +3
Physics and Astronomy · #Cosmology and Gravitation Theories #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #Dark Matter and Cosmic Phenomena #FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #Particle physics theoretical and experimental studies

paper · pdf · doi:10.48550/arxiv.1211.5129

openalex publication_date 2012/11/21 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

The complementarity between dark matter searches at colliders and in underground laboratories is an extraordinarily powerful tool in the quest for dark matter. In the vast majority of the analyses conducted so far these dark matter detection strategies have been profitably combined either to perform global fits in the context of certain particle physics models (e.g. the CMSSM) or to estimate the prospects for a direct dark matter detection given the LHC potential of discovering new physics beyond the Standard Model. In this paper we propose an alternative strategy to combine direct and collider dark matter searches: employing the potential of the upcoming generation of 1-ton direct detection experiments, we show that for certain supersymmetric configurations it is possible to translate the information encoded in an hypothetically discovered direct detection signal into classes of expected signals at the LHC. As an illustrative application of our method, we show that for a 60 GeV neutralino thermally produced via resonant annihilations and identified by a 1-ton direct detection experiment, our approach allows to forecast a clearly identifiable prediction for a LHC final state involving three leptons and missing energy. The strategy presented in this paper to systematically translate a direct detection signal into a prediction for the LHC has the potential to significantly strengthen the complementarity between these two dark matter detection strategies.

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