Speaker
Description
The evolution of the strongly interacting medium is modelled with hydrodynamic models, which are driven by a large number of parameters quantifying the properties of the medium. The need to find model parameters which give the best description of the experimental data imposes a multidimensional optimization problem. The Bayesian analysis has shown to be very effective in constraining the parameter values[1], and the combined inclusion of LHC Pb-Pb 5.02 and 2.76 TeV data with additional flow observables has greatly narrowed down the uncertainties[2].
In this talk, we present our latest study in inferring the transport properties of QGP by an improved Bayesian analysis using the RHIC Au-Au collision data in addition to the previous studies[1,2] where only the LHC data were used. Additionally, the initial conditions are in our study now described with a dynamical initial conditions model called EKRT. With the addition of RHIC data and the change of initial conditions model we aim to get a better understanding of the initial conditions' and transport properties' dependence on the system’s size and energy scale. Furthermore, we will quantify the sensitivities of newly developed flow observables, Asymmetric Cumulants and Symmetry Plane Correlations[3] as well as the $\rho (v_n^2, \, [p_T])$ correlation to model parameters.
[1] J. E. Parkkila, A. Onnerstad, and D. J. Kim. Phys. Rev. C, 104(5):054904, 2021
[2] J. E. Parkkila, A. Onnerstad, S. F. Taghavi, C. Mordasini, A. Bilandzic, M. Virta, and D.J. Kim. Physics Letters B, 835:137485, 2022.
[3] A. Bilandzic, M. Lesch, and S. F. Taghavi. Phys. Rev. C, 102:024910, 2020.
