Abstract
A search for dark matter is conducted in events with large missing transverse momentum and a hadronically decaying, Lorentz-boosted top quark. This study is performed using proton-proton collisions at a center-of-mass energy of 13 TeV, in data recorded by the CMS detector in 2016 at the LHC, corresponding to an integrated luminosity of 36 fb−1. New substructure techniques, including the novel use of energy correlation functions, are utilized to identify the decay products of the top quark. With no significant deviations observed from predictions of the standard model, limits are placed on the production of new heavy bosons coupling to dark matter particles. For a scenario with purely vector-like or purely axial-vector-like flavor changing neutral currents, mediator masses between 0.20 and 1.75 TeV are excluded at 95% confidence level, given a sufficiently small dark matter mass. Scalar resonances decaying into a top quark and a dark matter fermion are excluded for masses below 3.4 TeV, assuming a dark matter mass of 100 GeV.
Highlights
Hadronically decaying top quark identificationIf a hadronically decaying top quark is highly Lorentz-boosted, reconstructing the three daughter quarks separately becomes difficult, as the resulting jets tend to overlap in the detector
Expected and observed limits at 95% confidence level (CL) are set using the asymptotic approximation [57] of the CLs criterion [58, 59] with a profile likelihood ratio as the test statistic, in which systematic uncertainties are modeled as nuisance parameters
To allow for reinterpretation of the data in the context of signal models not considered in this paper, we provide the results of fitting data in the control regions (CR) and propagating the prediction to the signal region (SR) in appendix A
Summary
If a hadronically decaying top quark is highly Lorentz-boosted, reconstructing the three daughter quarks separately becomes difficult, as the resulting jets tend to overlap in the detector To identify such signatures, we define CA15 jets as objects that are clustered from PF candidates using the Cambridge-Aachen algorithm [30] with a distance parameter of 1.5. The “soft drop” (SD) [33] grooming method is used to remove soft and wide-angle radiation produced within jets through initial state radiation or through the underlying event Removing such radiation, the SD algorithm defines a subset of the CA15 jet’s constituents, which are further grouped into subjets of the CA15 jet. The chosen threshold corresponds to correctly identifying a bottom jet with a probability of 80%, and misidentifying a light-flavor jet with a probability of 10%
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