A search is presented for physics beyond the standard model (SM) using electron or muon pairs with high invariant mass. A data set of proton-proton collisions collected by the CMS experiment at the LHC at $$ \sqrt{s} $$ s = 13 TeV from 2016 to 2018 corresponding to a total integrated luminosity of up to 140 fb−1 is analyzed. No significant deviation is observed with respect to the SM background expectations. Upper limits are presented on the ratio of the product of the production cross section and the branching fraction to dileptons of a new narrow resonance to that of the Z boson. These provide the most stringent lower limits to date on the masses for various spin-1 particles, spin-2 gravitons in the Randall-Sundrum model, as well as spin-1 mediators between the SM and dark matter particles. Lower limits on the ultraviolet cutoff parameter are set both for four-fermion contact interactions and for the Arkani-Hamed, Dimopoulos, and Dvali model with large extra dimensions. Lepton flavor universality is tested at the TeV scale for the first time by comparing the dimuon and dielectron mass spectra. No significant deviation from the SM expectation of unity is observed.

Results are presented of an analysis of proton and charged pion azimuthal distributions measured with respect to the reaction plane in Au ϩ Au collisions at a beam momentum of about 11A GeV/c. The azimuthal anisotropy is studied as a function of particle rapidity and transverse momentum for different centralities of the collisions. The triple differential ͑in rapidity, transverse momentum, and azimuthal angle͒ distributions are reconstructed. A comparison of the results with a previous analysis of charged-particle and transverse energy flow as well as with model predictions are presented. ͓S0556-2813͑97͒05711-7͔ PACS number͑s͒: 25.75.Dw

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Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at √s = 13TeV, corresponding to an integrated luminosity of 35.9 fb−1. Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency.

Event shapes for Au + Au collisions at 11.4 GeV/c per nucleon were studied over nearly the full solid angle with the E877 apparatus. The analysis was performed by Fourier expansion of azimuthal distributions of the transverse energy (E T ) measured 1