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Thermal WIMPs and the Scale of New Physics: Global Fits of Dirac Dark Matter Effective Field Theories

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 نشر من قبل Ankit Beniwal
 تاريخ النشر 2021
  مجال البحث فيزياء
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We assess the status of a wide class of WIMP dark matter (DM) models in light of the latest experimental results using the global fitting framework $textsf{GAMBIT}$. We perform a global analysis of effective field theory (EFT) operators describing the interactions between a gauge-singlet Dirac fermion and the Standard Model quarks, the gluons and the photon. In this bottom-up approach, we simultaneously vary the coefficients of 14 such operators up to dimension 7, along with the DM mass, the scale of new physics and 8 nuisance parameters that reflect uncertainties in the local DM halo, nuclear form factors and the top quark mass. We include the renormalization group evolution of all operator coefficients and perform an automated matching to the non-relativistic EFT relevant for DM scattering. Our up-to-date likelihood functions include all relevant experimental constraints based on the latest data from $mathit{Planck}$, direct and indirect detection experiments, and the LHC, in particular a very recent ATLAS monojet search based on the full run 2 dataset. For light DM ($lesssim 100$ GeV), we find that it is impossible to satisfy all constraints simultaneously unless the particle under consideration constitutes only a DM sub-component and the scale of the new physics is so low that the EFT breaks down for the calculation of LHC constraints. At intermediate values of the new physics scale ($approx 1$ TeV), we find that our results are significantly influenced by several small excesses in the LHC data such that the best-fit parameter regions depend on the precise prescription that we adopt to ensure EFT validity. In addition to these interesting features, we find a large region of viable parameter space where the EFT is valid and the relic density can be reproduced, implying that WIMPs can still account for the DM of the universe while being consistent with the latest data.



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