The χ(b)(nP) quarkonium states are produced in proton-proton collisions at the Large Hadron Collider at sqrt[s] = 7  TeV and recorded by the ATLAS detector. Using a data sample corresponding to an integrated luminosity of 4.4  fb(-1), these states are reconstructed through their radiative decays to Υ(1S,2S) with Υ → μ+ μ-. In addition to the mass peaks corresponding to the decay modes χ(b)(1P,2P) → Υ(1S)γ, a new structure centered at a mass of 10.530 ± 0.005(stat) ± 0.009(syst)  GeV is also observed, in both the Υ(1S)γ and Υ(2S)γ decay modes. This structure is interpreted as the χ(b)(3P) system.

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A QCD analysis is reported of ATLAS data on inclusive W(±) and Z boson production in pp collisions at the LHC, jointly with ep deep-inelastic scattering data from HERA. The ATLAS data exhibit sensitivity to the light quark sea composition and magnitude at Bjorken x∼0.01. Specifically, the data support the hypothesis of a symmetric composition of the light quark sea at low x. The ratio of the strange-to-down sea quark distributions is determined to be 1.00(-0.28)(+0.25) at absolute four-momentum transfer squared Q(2)=1.9  GeV(2) and x=0.023.

We present an ultrafast neural network (NN) model, QLKNN, which predicts core tokamak transport heat and particle fluxes. QLKNN is a surrogate model based on a database of 300 million flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. The database covers a wide range of realistic tokamak core parameters. Physical features such as the existence of a critical gradient for the onset of turbulent transport were integrated into the neural network training methodology. We have coupled QLKNN to the tokamak modelling framework JINTRAC and rapid control-oriented tokamak transport solver RAPTOR. The coupled frameworks are demonstrated and validated through application to three JET shots covering a representative spread of H-mode operating space, predicting turbulent transport of energy and particles in the plasma core. JINTRAC-QLKNN and RAPTOR-QLKNN are able to accurately reproduce JINTRAC-QuaLiKiz T i,e and n e profiles, but 3 to 5 orders of magnitude faster. Simulations which take hours are reduced down to only a few tens of seconds. The discrepancy in the final source-driven predicted profiles between QLKNN and QuaLiKiz is on the order 1%-15%. Also the dynamic behaviour was well captured by QLKNN, with differences of only 4%-10% compared to JINTRAC-QuaLiKiz observed at mid-radius, for a study of density buildup following the L-H transition. Deployment of neural network surrogate models in multi-physics integrated tokamak modelling is a promising route towards enabling accurate and fast tokamak scenario optimization, Uncertainty Quantification, and control applications.

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