Towards the determination of heavy-quark transport coefficients in quark-gluon plasma
Shanshan Cao, Gabriele Coci, Santosh Kumar Das, Weiyao Ke, Shuai Y.F., Liu, Salvatore Plumari, Taesoo Song, Yingru Xu, J\"org Aichelin, Steffen, Bass, Elena Bratkovskaya, Xing Dong, Pol Bernard Gossiaux, Vincenzo Greco,, Min He, Marlene Nahrgang, Ralf Rapp, Francesco Scardina

TL;DR
This paper systematically compares heavy-quark transport coefficients across various models in quark-gluon plasma, reducing uncertainties and highlighting the importance of realistic hydrodynamics and hadronization in interpreting heavy-flavor data.
Contribution
It provides a unified scheme to compare transport coefficients, significantly reducing the systematic uncertainties and emphasizing the role of realistic medium evolution and hadronization processes.
Findings
Systematic comparison reduces drag coefficient uncertainty to a factor of 2.
Realistic hydrodynamic evolution constrains heavy-quark transport coefficients.
Additional constraints can further decrease theoretical uncertainties.
Abstract
Several transport models have been employed in recent years to analyze heavy-flavor meson spectra in high-energy heavy-ion collisions. Heavy-quark transport coefficients extracted from these models with their default parameters vary, however, by up to a factor of 5 at high momenta. To investigate the origin of this large theoretical uncertainty, a systematic comparison of heavy-quark transport coefficients is carried out between various transport models. Within a common scheme devised for the nuclear modification factor of charm quarks in a brick medium of a quark-gluon plasma, the systematic uncertainty of the extracted drag coefficient among these models is shown to be reduced to a factor of 2, which can be viewed as the smallest intrinsic systematical error band achievable at present time. This indicates the importance of a realistic hydrodynamic evolution constrained by bulk hadron…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
