Interplay between bulk medium evolution and (D)GLV energy loss

Abstract

We study the consistency between high-pT nuclear suppression (R_AA) and elliptic flow (v2) using Gyulassy-Levai-Vitev (GLV) energy loss or a simpler power-law dE/dL formula, for a variety of bulk evolution models. The results generally confirm our earlier work [arXiv:1305.1046] that found suppressed elliptic flow for transversely expanding media. One exception is the set of hydrodynamic solutions used recently [arXiv:1305.6458] by Betz and Gyulassy, which give significantly higher v2 but unfortunately assume unrealistic bag-model equation of state. On the other hand, we show that covariant treatment of energy loss introduces an interplay between jet direction and hydrodynamic flow of the medium, which largely counteracts elliptic flow suppression caused by transverse expansion.

Presentation at Hard Probes 2013 (4 pages, 8 PDF plots, Elsevier style file)

References in corpus (4)

• How does transverse (hydrodynamic) flow affect jet-broadening and jet-quenching ?
• Radiative heavy quark energy loss in a dynamical QCD medium
• High-pT suppression and elliptic flow from radiative energy loss with realistic bulk medium expansion
• Azimuthal Jet Tomography of Quark Gluon Plasmas at RHIC and LHC

Cited by in corpus (8)

• DREENA-B framework: first predictions of $R_{AA}$ and $v_2$ within dynamical energy loss formalism in evolving QCD medium
• Heavy flavor suppression predictions at 5.1 TeV Pb+Pb collisions at LHC
• Exploring the initial stages in heavy-ion collisions with high-pT RAA and v2 theory and data
• How to test path-length dependence in energy loss mechanisms: analysis leading to a new observable
• Extracting the temperature dependence in high-$p_{\perp }$ particle energy loss
• Shape of the quark gluon plasma droplet reflected in the high-p_T data
• Dynamical energy loss formalism: from describing suppression patterns to implications for future experiments
• Understanding mass hierarchy in collisional energy loss through heavy flavor data