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Chiral phonons and their strong coupling to spins reveal unconventional interlayer exchange interaction and resultant spin state. 

Indium tin oxide (ITO) is one of the most widely used transparent conductors in optoelectronic device applications. We investigated the optical properties of ITO thin films at high temperatures up to 800 °C using spectroscopic ellipsometry. As temperature increases, amorphous ITO thin films undergo a phase transition at ~ 200 °C and develop polycrystalline phases with increased optical gap energies. The optical gap energies of both polycrystalline and epitaxial ITO thin films decrease with increasing temperature due to electron–phonon interactions. Depending on the background oxygen partial pressure, however, we observed that the optical gap energies exhibit reversible changes, implying that the oxidation and reduction processes occur vigorously due to the low oxidation and reduction potential energies of the ITO thin films at high temperatures. This result suggests that the electronic structure of ITO thin films strongly depends on temperature and oxygen partial pressure while they remain optically transparent, i.e., optical gap energies > 3.6 eV.

 

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.