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We study the effect of thermal charm production on charmonium regeneration in high energy nuclear collisions. By solving the kinetic equations for charm quark and charmonium distributions in Pb+Pb collisions, we calculate the global and differential nuclear modification factors RAA(Npart) and RAA(pt) for J/ψ s. Due to the thermal charm production in hot medium, the charmonium production source changes from the initially created charm quarks at SPS, RHIC and LHC to the thermally produced charm quarks at Future Circular Collider (FCC), and the J/ψ suppression (RAA<1) observed so far will be replaced by a strong enhancement (RAA>1) at FCC at low transverse momentum.
One of important consequences of Hagedorn statistical bootstrap model is the prediction of limiting temperature Tcrit for hadron systems colloquially known as Hagedorn temperature. According to Hagedorn, this effect should be observed in hadron spectra obtained in infinite equilibrated nuclear matter rather than in relativistic heavy-ion collisions. We present results of microscopic model calculations for the infinite nuclear matter, simulated by a box with periodic boundary conditions. The limiting temperature indeed appears in the model calculations. Its origin is traced to strings and many-body decays of resonances.
In the present work we study the effect of unparticle modified static potentials on the energy levels of the hydrogen atom. By using Rayleigh–Schrödinger perturbation theory, we obtain the energy shift of the ground state and compare it with experimental data. Bounds on the unparticle energy scale U as a function of the scaling dimension and the coupling constant λ are derived. We show that there exists a parameter region where bounds on U ar are stringent, signaling that unparticles could be tested in atomic physics experiments.
The ALICE Collaboration is collecting data with both Minimum Bias and Muon triggers with pp collisions at √s = 13 TeV in the ongoing LHC Run II. An excellent performance of tracking and PID in the central barrel and in the muon spectrometer has been obtained. First results on the charged-particle pseudorapidity density and on identified particle transverse momentum spectra at √s = 13 TeV is presented.
ALICE is the dedicated heavy-ion experiment at the Large Hadron Collider at CERN. After a two-year long shutdown, the LHC restarted its physics programme in June 2015 with proton-proton collisions at √s = 13 TeV and Pb-Pb collisions at √sNN = 5.02 TeV, the highest centre-of-mass energy ever reached in laboratory. Recent results and future perspective for ALICE will be presented.
Measurements of the transverse momentum spectra of light flavor particles at intermediate and high pT are an important tool for QCD studies. In pp collisions they provide a baseline for perturbative QCD, while in Pb–Pb they are used to investigate the suppression caused by the surrounding medium. In p–Pb collisions, such measurements provide a reference to disentangle final from initial state effects and thus play an important role in the search for signatures of the formation of a deconfined hot medium. While the comparison of the p–Pb and Pb–Pb data indicates that initial state effects do not play a role in the suppression of hadron production observed at high pT in heavy ion collisions, several measurements of particle production in the low and intermediate pT region indicate the presence of collective effects.
Hadronic polarization and the related anisotropy of the dilepton angular distribution are studied for the reaction πN→Ne+e−. We employ consistent effective interactions for baryon resonances up to spin-5/2, where non-physical degrees of freedom are eliminated, to compute the anisotropy coefficients for isolated intermediate baryon resonances. It is shown that the spin and parity of the intermediate baryon resonance is reflected in the angular dependence of the anisotropy coefficient. We then compute the anisotropy coefficient including the N(1520) and N(1440) resonances, which are essential at the collision energy of the recent data obtained by the HADES Collaboration on this reaction. We conclude that the anisotropy coefficient provides useful constraints for unraveling the resonance contributions to this process.
The detailed biophysical mechanisms through which transcranial magnetic stimulation (TMS) activates cortical circuits are still not fully understood. Here we present a multi-scale computational model to describe and explain the activation of different cell types in motor cortex due to transcranial magnetic stimulation. Our model determines precise electric fields based on an individual head model derived from magnetic resonance imaging and calculates how these electric fields activate morphologically detailed models of different neuron types. We predict detailed neural activation patterns for different coil orientations consistent with experimental findings. Beyond this, our model allows us to predict activation thresholds for individual neurons and precise initiation sites of individual action potentials on the neurons’ complex morphologies. Specifically, our model predicts that cortical layer 3 pyramidal neurons are generally easier to stimulate than layer 5 pyramidal neurons, thereby explaining the lower stimulation thresholds observed for I-waves compared to D-waves. It also predicts differences in the regions of activated cortical layer 5 and layer 3 pyramidal cells depending on coil orientation. Finally, it predicts that under standard stimulation conditions, action potentials are mostly generated at the axon initial segment of corctial pyramidal cells, with a much less important activation site being the part of a layer 5 pyramidal cell axon where it crosses the boundary between grey matter and white matter. In conclusion, our computational model offers a detailed account of the mechanisms through which TMS activates different cortical cell types, paving the way for more targeted application of TMS based on individual brain morphology in clinical and basic research settings.
High shares of intermittent renewable power generation in a European electricity system will require flexible backup power generation on the dominant diurnal, synoptic, and seasonal weather timescales. The same three timescales are already covered by today’s dispatchable electricity generation facilities, which are able to follow the typical load variations on the intra-day, intra-week, and seasonal timescales. This work aims to quantify the changing demand for those three backup flexibility classes in emerging large-scale electricity systems, as they transform from low to high shares of variable renewable power generation. A weather-driven modelling is used, which aggregates eight years of wind and solar power generation data as well as load data over Germany and Europe, and splits the backup system required to cover the residual load into three flexibility classes distinguished by their respective maximum rates of change of power output. This modelling shows that the slowly flexible backup system is dominant at low renewable shares, but its optimized capacity decreases and drops close to zero once the average renewable power generation exceeds 50% of the mean load. The medium flexible backup capacities increase for modest renewable shares, peak at around a 40% renewable share, and then continuously decrease to almost zero once the average renewable power generation becomes larger than 100% of the mean load. The dispatch capacity of the highly flexible backup system becomes dominant for renewable shares beyond 50%, and reach their maximum around a 70% renewable share. For renewable shares above 70% the highly flexible backup capacity in Germany remains at its maximum, whereas it decreases again for Europe. This indicates that for highly renewable large-scale electricity systems the total required backup capacity can only be reduced if countries share their excess generation and backup power.
ALICE (A Large Heavy Ion Experiment) is one of the four large scale experiments at the Large Hadron Collider (LHC) at CERN. The High Level Trigger (HLT) is an online computing farm, which reconstructs events recorded by the ALICE detector in real-time. The most computing-intensive task is the reconstruction of the particle trajectories. The main tracking devices in ALICE are the Time Projection Chamber (TPC) and the Inner Tracking System (ITS). The HLT uses a fast GPU-accelerated algorithm for the TPC tracking based on the Cellular Automaton principle and the Kalman filter. ALICE employs gaseous subdetectors which are sensitive to environmental conditions such as ambient pressure and temperature and the TPC is one of these. A precise reconstruction of particle trajectories requires the calibration of these detectors. As our first topic, we present some recent optimizations to our GPU-based TPC tracking using the new GPU models we employ for the ongoing and upcoming data taking period at LHC. We also show our new approach to fast ITS standalone tracking. As our second topic, we present improvements to the HLT for facilitating online reconstruction including a new flat data model and a new data flow chain. The calibration output is fed back to the reconstruction components of the HLT via a feedback loop. We conclude with an analysis of a first online calibration test under real conditions during the Pb-Pb run in November 2015, which was based on these new features.