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Dark Matter and How To Find It: A search for low-mass leptophobic Dark Matter mediators and the development of mass-decorrelated jet taggers with the ATLAS experiment
A search for low-mass leptophobic Dark Matter (DM) mediator particles in $36~\mathrm{fb}^{-1}$ of $pp$ collision data at $\sqrt{s} = 13~\mathrm{TeV}$ collected by the ATLAS experiment is presented. The search is performed in final states where the mediator decay into a quark pair is reconstructed as...
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Lenguaje: | eng |
Publicado: |
2019
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Materias: | |
Acceso en línea: | http://cds.cern.ch/record/2690397 |
Sumario: | A search for low-mass leptophobic Dark Matter (DM) mediator particles in $36~\mathrm{fb}^{-1}$ of $pp$ collision data at $\sqrt{s} = 13~\mathrm{TeV}$ collected by the ATLAS experiment is presented. The search is performed in final states where the mediator decay into a quark pair is reconstructed as a single, large-radius jet produced in association with a photon or a jet. No deviations from the Standard Model expectation are observed, and limits are placed on the production cross-section of leptophobic mediator particles and their coupling to quarks for mediator masses between $100$ and $220~\mathrm{GeV}$. At the time of publication, this result constituted the lowest limits on leptophobic DM mediator masses for high-mass DM particles reported by ATLAS. Adversarial neural networks (ANN) are presented as a way to train jet taggers which decorrelates them from the invariant mass of the jet. An extensive study of five different approaches to constructing mass-decorrelated jet taggers is presented. The ANN tagger is found to provide the largest QCD multijet rejection at similar levels of mass-decorrelation. |
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