Abstract
A perturbative QCD based jet tomographic Monte Carlo model, CUJET2.0, is presented to predict jet quenching observables in relativistic heavy ion collisions at RHIC/BNL and LHC/CERN energies. This model generalizes the DGLV theory of flavor dependent radiative energy loss by including multi-scale running strong coupling effects. It generalizes CUJET1.0 by computing jet path integrations though more realistic 2 + 1D transverse and longitudinally expanding viscous hydrodynamical fields contrained by fits to low p T flow data. The CUJET2.0 output depends on three control parameters, (α max, f E, f M ), corresponding to an assumed upper bound on the vacuum running coupling in the infrared and two chromo-electric and magnetic QGP screening mass scales (f E μ(T), f M μ(T)) where μ(T) is the 1-loop Debye mass. We compare numerical results as a function of α max for pure and deformed HTL dynamically enhanced scattering cases corresponding to (f E = 1, 2, f M = 0) to data of the nuclear modification factor, R AAf (p T, √s, b) for jet fragment flavors f = π, D, B, e at s = 0.2 - 2.76 ATeV c.m. energies per nucleon pair and with impact parameter b = 2.4, 7.5 fm. A χ 2 analysis is presented and shows that R AAπ data from RHIC and LHC are consistent with CUJET2.0 at the χ 2 /d.o.f < 2 level for α max = 0.23 - 0.30. The corresponding q (E jet, T)/T 3 effective jet transport coefficient field of this model is computed to facilitate comparison to other jet tomographic models in the literature. The predicted elliptic asymmetry, v 2(p T ; s, b) is, however, found to significantly underestimated relative to RHIC and LHC data. We find the χv22 analysis shows that v 2 is very sensitive to allowing even as little as 10% variations of the path averaged α max along in and out of reaction plane paths. © 2014 The Author(s).
Author supplied keywords
Cite
CITATION STYLE
Xu, J., Buzzatti, A., & Gyulassy, M. (2014). Azimuthal jet flavor tomography with CUJET2.0 of nuclear collisions at RHIC and LHC. Journal of High Energy Physics, 2014(8). https://doi.org/10.1007/JHEP08(2014)063
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.