arXiv:2404.16082v1 Announce Type: new Abstract: A measurement is presented of a ratio observable that provides a measure of the azimuthal correlations among jets with large transverse momentum $p_\mathrm{T}$. This observable is measured in multijet events over the range of $p_\mathrm{T}$ = 360-3170 GeV based on data collected by the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 134 fb$^{-1}$. The results are compared with predictions from Monte Carlo parton-shower event generator simulations, as well as with fixed-order perturbative quantum chromodynamics (pQCD) predictions at next-to-leading-order (NLO) accuracy obtained with different parton distribution functions (PDFs) and corrected for nonperturbative and electroweak effects. Data and theory agree within uncertainties. From the comparison of the measured observable with the pQCD prediction obtained with the NNPDF3.1 NLO PDFs, the strong coupling at the Z boson mass scale is $\alpha_\mathrm{S}(m_\mathrm{Z})$ = 0.1177 $\pm$ 0.0013 (exp) $_{-0.0073}^{+0.0116}$ (theo) = 0.1177$_{-0.0074}^{+0.0117}$, where the total uncertainty is dominated by the scale dependence of the fixed-order predictions. A test of the running of $\alpha_\mathrm{S}(m_\mathrm{Z})$ in the TeV region shows no deviation from the expected NLO pQCD behaviour.
arXiv:2404.16091v1 Announce Type: cross Abstract: Machine learning has become an essential tool in jet physics. Due to their complex, high-dimensional nature, jets can be explored holistically by neural networks in ways that are not possible manually. However, innovations in all areas of jet physics are proceeding in parallel. We show that specially constructed machine learning models trained for a specific jet classification task can improve the accuracy, precision, or speed of all other jet physics tasks. This is demonstrated by training on a particular multiclass classification task and then using the learned representation for different classification tasks, for datasets with a different (full) detector simulation, for jets from a different collision system ($pp$ versus $ep$), for generative models, for likelihood ratio estimation, and for anomaly detection. Our OmniLearn approach is thus a foundation model and is made publicly available for use in any area where state-of-the-art precision is required for analyses involving jets and their substructure.
arXiv:2404.16094v1 Announce Type: cross Abstract: We demonstrate that long-ranged terrestrial electric fields can be used to exclude or discover ultralight bosonic particles with extremely small charge, beyond that probed by astrophysics. Bound condensates of scalar millicharged particles can be rapidly produced near electrostatic generators or in the atmosphere. If such particles directly couple to the photon, they quickly short out such electrical activity. Instead, for interactions mediated by a kinetically-mixed dark photon, the effects of this condensate are suppressed depending on the size of the kinetic mixing, but may still be directly detected with precision electromagnetic sensors. Analogous condensates can also develop in other theories involving new long-ranged forces, such as those coupled to baryon and lepton number.