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In this review, we consider a general theoretical framework for fermionic color-singlet states—including a singlet, a doublet, and a triplet under the Standard Model SU(2) L gauge symmetry, corresponding to the bino, higgsino, and wino in supersymmetric theories—generically dubbed electroweakinos for their mass eigenstates. Depending on the relations among these states’ three mass parameters and their mixing after the electroweak symmetry breaking, this sector leads to a rich phenomenology that may be accessible in current and near-future experiments. We discuss the decay patterns of electroweakinos and their observable signatures at colliders, review the existing bounds on the model parameters, and summarize the current statuses of the comprehensive searches by the ATLAS and CMS Collaborations at the Large Hadron Collider. We also comment on the prospects for future colliders. An important feature of the theory is that the lightest neutral electroweakino can be identified as a weakly interacting massive particle cold dark matter candidate. We take into account the existing bounds on the parameters from the dark matter direct detection experiments and discuss the complementarity of the electroweakino searches at colliders. 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.