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Within a multi-phase transport model with string melting scenario, jet transport parameter $hat{q}$ is calculated in Au+Au collisions at $sqrt{s_{NN} } $= 200 GeV and Pb+Pb collisions at $sqrt{s_{NN} } $= 2.76 TeV. The $hat{q}$ increases with the increasing of jet energy for both partonic phase and hadronic phase. The energy and path length dependences of $hat{q}$ in full heavy-ion evolution are consistent with the expectations of jet quenching. The correlation between jet transport parameter $hat{q}$ and dijet transverse momentum asymmetry $A_{J}$ is mainly investigated, which discloses that a larger $hat{q}$ corresponds to a larger $A_{J}$. It supports a consistent jet energy loss picture from the two viewpoints of single jet and dijet. It is proposed to measure dijet asymmetry distributions with different jet transport parameter ranges as a new potential method to study jet quenching physics in high energy heavy-ion collisions.
We report the effect of magnetic field on estimation of jet transport coefficient, $hat{q}$ using a simplified quasi-particle model. Our adopted quasi-particle model introduces temperature and magnetic field dependent degeneracy factors of partons, w
Jet-medium interaction involves two important effects: jet energy loss and medium response. The search for jet-induced medium excitations is one of the hot topics in jet quenching study in relativistic nuclear collisions. In this work, we perform a s
$alpha$-clustered structures in light nuclei could be studied through snapshots taken by relativistic heavy-ion collisions. A multiphase transport (AMPT) model is employed to simulate the initial structure of collision nuclei and the proceeding colli
We report a new determination of $hat{q}$, the jet transport coefficient of the Quark-Gluon Plasma. We use the JETSCAPE framework, which incorporates a novel multi-stage theoretical approach to in-medium jet evolution and Bayesian inference for param
We present a new determination of $hat{q}$, the jet transport coefficient of the quark-gluon plasma. Using the JETSCAPE framework, we use Bayesian parameter estimation to constrain the dependence of $hat{q}$ on the jet energy, virtuality, and medium