Measurements of the charge asymmetry in top-quark pair production in the dilepton final state at s √ =8 TeV with the ATLAS detector

Measurements of the top-antitop quark pair production charge asymmetry in the dilepton channel, characterized by two high-pT leptons (electrons or muons), are presented using data corresponding to an integrated luminosity of 20.3  fb−1 from pp collisions at a center-of-mass energy s√=8  TeV collected with the ATLAS detector at the Large Hadron Collider at CERN. Inclusive and differential measurements as a function of the invariant mass, transverse momentum, and longitudinal boost of the tt¯ system are performed both in the full phase space and in a fiducial phase space closely matching the detector acceptance. Two observables are studied: AℓℓC based on the selected leptons and Att¯C based on the reconstructed tt¯ final state. The inclusive asymmetries are measured in the full phase space to be AℓℓC=0.008±0.006 and Att¯C=0.021±0.016, which are in agreement with the Standard Model predictions of AℓℓC=0.0064±0.0003 and Att¯C=0.0111±0.0004

Disruption prediction with artificial intelligence techniques in tokamak plasmas

In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100 million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape of a torus. Light nuclei, such as deuterium and tritium, undergo a fusion reaction that releases energy, making fusion a promising option for a sustainable and clean energy source. Tokamak plasmas, however, are prone to disruptions as a result of a sudden collapse of the system terminating the fusion reactions. As disruptions lead to an abrupt loss of confinement, they can cause irreversible damage to present-day fusion devices and are expected to have a more devastating effect in future devices. Disruptions expected in the next-generation tokamak, ITER, for example, could cause electromagnetic forces larger than the weight of an Airbus A380. Furthermore, the thermal loads in such an event could exceed the melting threshold of the most resistant state-of-the-art materials by more than an order of magnitude. To prevent disruptions or at least mitigate their detrimental effects, empirical models obtained with artificial intelligence methods, of which an overview is given here, are commonly employed to predict their occurrence—and ideally give enough time to introduce counteracting measures

Runaway electron beam control

Post-disruption runaway electron (RE) beams in tokamaks with large current can cause deep melting of the vessel and are one of the major concerns for ITER operations. Consequently, a considerable effort is provided by the scientific community in order to test RE mitigation strategies. We present an overview of the results obtained at FTU and TCV controlling the current and position of RE beams to improve safety and repeatability of mitigation studies such as massive gas (MGI) and shattered pellet injections (SPI). We show that the proposed RE beam controller (REB-C) implemented at FTU and TCV is effective and that current reduction of the beam can be performed via the central solenoid reducing the energy of REs, providing an alternative/parallel mitigation strategy to MGI/SPI. Experimental results show that, meanwhile deuterium pellets injected on a fully formed RE beam are ablated but do not improve RE energy dissipation rate, heavy metals injected by a laser blow off system on low-density flat-top discharges with a high level of RE seeding seem to induce disruptions expelling REs. Instabilities during the RE beam plateau phase have shown to enhance losses of REs, expelled from the beam core. Then, with the aim of triggering instabilities to increase RE losses, an oscillating loop voltage has been tested on RE beam plateau phase at TCV revealing, for the first time, what seems to be a full conversion from runaway to ohmic current. We finally report progresses in the design of control strategies at JET in view of the incoming SPI mitigation experiments

Real-time-capable prediction of temperature and density profiles in a tokamak using RAPTOR and a first-principle-based transport model

The RAPTOR code is a control-oriented core plasma profile simulator with various applications in control design and verification, discharge optimization and real-time plasma simulation. To date, RAPTOR was capable of simulating the evolution of poloidal flux and electron temperature using empirical transport models, and required the user to input assumptions on the other profiles and plasma parameters. We present an extension of the code to simulate the temperature evolution of both ions and electrons, as well as the particle density transport. A proof-of-principle neural-network emulation of the quasilinear gyrokinetic QuaLiKiz transport model is coupled to RAPTOR for the calculation of first-principle-based heat and particle turbulent transport. These extended capabilities are demonstrated in a simulation of a JET discharge. The multi-channel simulation requires ∼0.2 s to simulate 1 second of a JET plasma, corresponding to ∼20 energy confinement times, while predicting experimental profiles within the limits of the transport model. The transport model requires no external inputs except for the boundary condition at the top of the H-mode pedestal. This marks the first time that simultaneous, accurate predictions of Te, Tiand nehave been obtained using a first-principle-based transport code that can run in faster-than-real-time for present-day tokamaks