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The irradiation of the CMS Hadron Calorimeter (HCAL) subdetectors results in decreased signal output from the active materials as well as increased noise in the photodetectors used to read out the system. The HCAL has a dedicated calibration system used to monitor and correct for these effects and to help synchronise the timing of the subdetectors. The calibration system is described with a focus on the upgrades to the laser system, which has undergone significant changes since the end of Run 2 of the LHC in 2018. A new solid state laser has been installed and commissioned, and the optical setup for light distribution has been simplified. An upgrade to the laser trigger board has reduced the laser trigger jitter by an order of magnitude. A new system has also been developed to fire the laser, based on existing HCAL electronics. Future improvements to the system are also presented, including ongoing work on extending the system to include remote monitoring capabilities. 

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.