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A bstract The identity of Dark Matter (DM) is one of the most active topics in particle physics today. Supersymmetry (SUSY) is an extension of the standard model (SM) that could describe the particle nature of DM in the form of the lightest neutralino in R-parity conserving models. We focus on SUSY models that solve the hierarchy problem with small fine tuning, and where the lightest SUSY particles $$ \left({\tilde{\upchi}}_1^0,{\tilde{\upchi}}_1^{\pm },{\tilde{\upchi}}_2^0\right) $$ χ ˜ 1 0 χ ˜ 1 ± χ ˜ 2 0 are a triplet of higgsino-like states, such that the mass difference $$ \Delta m\left({\tilde{\upchi}}_2^0,{\tilde{\upchi}}_1^0\right) $$ Δ m χ ˜ 2 0 χ ˜ 1 0 is 0.5–50 GeV. We perform a feasibility study to assess the long-term discovery potential for these compressed SUSY models with higgsino-like states, using vector boson fusion (VBF) processes in the context of proton-proton collisions at $$ \sqrt{s} $$ s = 13 TeV, at the CERN Large Hadron Collider. Assuming an integrated luminosity of 3000 fb − 1 , we find that stringent VBF requirements, combined with large missing momentum and one or two low- p T leptons, is effective at reducing the major SM backgrounds, leading to a 5 σ (3 σ ) discovery reach for $$ m\left({\tilde{\upchi}}_2^0\right) $$ m χ ˜ 2 0 < 180 (260) GeV, and a projected 95% confidence level exclusion region that covers $$ m\left({\tilde{\upchi}}_2^0\right) $$ m χ ˜ 2 0 up to 385 GeV, parameter space that is currently unconstrained by other experiments. 

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.