Quantifying jet transport properties via large pTp_T hadron production

Nuclear modification factor $R_{AA}$ for large $p_T$ single hadron is studied in a next-to-leading order (NLO) perturbative QCD (pQCD) parton model with medium-modified fragmentation functions (mFFs) due to jet quenching in high-energy heavy-ion collisions. The energy loss of the hard partons in the QGP is incorporated in the mFFs which utilize two most important parameters to characterize the transport properties of the hard parton jets: the jet transport parameter $\hat q_{0}$ and the mean free path $\lambda_{0}$, both at the initial time $\tau_0$. A phenomenological study of the experimental data for $R_{AA}(p_{T})$ is performed to constrain the two parameters with simultaneous $\chi^2/{\rm d.o.f}$ fits to RHIC as well as LHC data. We obtain for energetic quarks $\hat q_{0}\approx 1.1 \pm 0.2$ GeV$^2$/fm and $\lambda_{0}\approx 0.4 \pm 0.03$ fm in central $Au+Au$ collisions at $\sqrt{s_{NN}}=200$ GeV, while $\hat q_{0}\approx 1.7 \pm 0.3$ GeV$^2$/fm, and $\lambda_{0}\approx 0.5 \pm 0.05$ fm in central $Pb+Pb$ collisions at $\sqrt{s_{NN}}=2.76$ TeV. Numerical analysis shows that the best fit favors a multiple scattering picture for the energetic jets propagating through the bulk medium, with a moderate averaged number of gluon emissions. Based on the best constraints for $\lambda_{0}$ and $\tau_0$, the estimated value for the mean-squared transverse momentum broadening is moderate which implies that the hard jets go through the medium with small reflection.Comment: 8 pages, 6 figures, revised versio

Predicting the epidemic threshold of the susceptible-infected-recovered model

Researchers have developed several theoretical methods for predicting epidemic thresholds, including the mean-field like (MFL) method, the quenched mean-field (QMF) method, and the dynamical message passing (DMP) method. When these methods are applied to predict epidemic threshold they often produce differing results and their relative levels of accuracy are still unknown. We systematically analyze these two issues---relationships among differing results and levels of accuracy---by studying the susceptible-infected-recovered (SIR) model on uncorrelated configuration networks and a group of 56 real-world networks. In uncorrelated configuration networks the MFL and DMP methods yield identical predictions that are larger and more accurate than the prediction generated by the QMF method. When compared to the 56 real-world networks, the epidemic threshold obtained by the DMP method is closer to the actual epidemic threshold because it incorporates full network topology information and some dynamical correlations. We find that in some scenarios---such as networks with positive degree-degree correlations, with an eigenvector localized on the high $k$-core nodes, or with a high level of clustering---the epidemic threshold predicted by the MFL method, which uses the degree distribution as the only input parameter, performs better than the other two methods. We also find that the performances of the three predictions are irregular versus modularity