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A bstract Searches for new low-mass matter and mediator particles have actively been pursued at fixed target experiments and at e + e − colliders. It is challenging at the CERN LHC, but they have been searched for in Higgs boson decays and in B meson decays by the ATLAS and CMS Collaborations, as well as in a low transverse momentum phenomena from forward scattering processes (e.g., FASER). We propose a search for a new scalar particle in association with a heavy vector-like quark. We consider the scenario in which the top quark ( t ) couples to a light scalar ϕ′ and a heavy vector-like top quark T . We examine single and pair production of T in pp collisions, resulting in a final state with a top quark that decays purely hadronically, a T which decays semileptonically ( T → W + b → ℓ ν b ), and a ϕ′ that is very boosted and decays to a pair of collimated photons which can be identified as a merged photon system. The proposed search is expected to achieve a discovery reach with signal significance greater than 5 σ (3 σ ) for m ( T ) as large as 1.8 (2) TeV and m ( ϕ′ ) as small as 1 MeV, assuming an integrated luminosity of 3000 fb − 1 . This search can expand the reach of T , and demonstrates that the LHC can probe low-mass, MeV-scale particles. 

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.