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Abstract Tuning and reconfiguring of nanophotonic components are needed to realize systems incorporating many components. The electrostatic force can deform a structure and tune its optical response. Despite the success of electrostatic actuators, they suffer from trade-offs between tuning voltage, tuning range, and on-chip area. Piezoelectric actuation could resolve these challenges, but only pm-per-volt scale wavelength tunability has been achieved. Here we propose and demonstrate compact piezoelectric actuators, called nanobenders, that transduce tens of nanometers per volt. By leveraging the non-uniform electric field from submicron electrodes, we generate bending of a piezoelectric nanobeam. Combined with a sliced photonic crystal cavity to sense displacement, we show tuning of an optical resonance by ~ 5 nm V⁻¹ (0.6 THz V⁻¹) and between 1520 ~ 1560 nm (~ 400 linewidths) within 4 V. Finally, we consider tunable nanophotonic components enabled by the nanobenders. 

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.