Jonah E. Bernhard

Applying Bayesian parameter estimation to relativistic heavy-ion collisions: simultaneous characterization of the initial state and quark-gluon plasma medium

We quantitatively estimate properties of the quark-gluon plasma created in ultrarelativistic heavy-ion collisions utilizing Bayesian statistics and a multiparameter model-to-data comparison. The study is performed using a recently developed parametric initial condition model, TRENTo, which interpolates among a general class of particle production schemes, and a modern hybrid model which couples viscous hydrodynamics to a hadronic cascade. We calibrate the model to multiplicity, transverse momentum, and flow data and report constraints on the parametrized initial conditions and the temperature-dependent transport coefficients of the quark-gluon plasma. We show that initial entropy deposition is consistent with a saturation-based picture, extract a relation between the minimum value and slope of the temperature-dependent specific shear viscosity, and find a clear signal for a nonzero bulk viscosity.

The iEBE-VISHNU code package for relativistic heavy-ion collisions

The iEBE-VISHNU code package performs event-by-event simulations for relativistic heavy-ion collisions using a hybrid approach based on (2+1)-dimensional viscous hydrodynamics coupled to a hadronic cascade model. We present the detailed model implementation, accompanied by some numerical code tests for the package. iEBE-VISHNU forms the core of a general theoretical framework for model-data comparisons through large scale Monte-Carlo simulations. A numerical interface between the hydrodynamically evolving medium and thermal photon radiation is also discussed. This interface is more generally designed for calculations of all kinds of rare probes that are coupled to the temperature and flow velocity evolution of the bulk medium, such as jet energy loss and heavy quark diffusion.

Quantifying properties of hot and dense QCD matter through systematic model-to-data comparison

We systematically compare an event-by-event heavy-ion collision model to data from the CERN Large Hadron Collider. Using a general Bayesian method, we probe multiple model parameters including fundamental quark-gluon plasma properties such as the specific shear viscosity η/s, calibrate the model to optimally reproduce experimental data, and extract quantitative constraints for all parameters simultaneously. Furthermore, the method is universal and easily extensible to other data and collision models.

Constraints on rapidity-dependent initial conditions from charged particle pseudorapidity densities and two-particle correlations

We study the initial three-dimensional spatial configuration of the quark-gluon plasma produced in relativistic heavy-ion collisions using centrality and rapidity-dependent measurements of charged particle pseudorapidity densities and two-particle correlations. A cumulant-generating function is used to parametrize the rapidity dependence of local entropy deposition and extend arbitrary boost-invariant initial conditions to nonzero beam rapidities. The model is compared to p+Pb and Pb+Pb single-particle distributions and systematically optimized using Bayesian parameter estimation to extract high-probability initial condition parameters. The optimized initial conditions are then compared to a number of experimental observables including two-particle rapidity correlations, the rapidity dependence of anisotropic flow, and event-plane decorrelations.

Determination of Quark-Gluon-Plasma Parameters from a Global Bayesian Analysis

Abstract The quality of data taken at RHIC and LHC as well as the success and sophistication of computational models for the description of ultra-relativistic heavy-ion collisions have advanced to a level that allows for the quantitative extraction of the transport properties of the Quark-Gluon-Plasma. However, the complexity of this task as well as the computational effort associated with it can only be overcome by developing novel methodologies: in this paper we outline such an analysis based on Bayesian Statistics and systematically compare an event-by-event heavy-ion collision model to data from the Large Hadron Collider. We simultaneously probe multiple model parameters including fundamental quark-gluon plasma properties such as the temperature-dependence of the specific shear viscosity η / s , calibrate the model to optimally reproduce experimental data, and extract quantitative constraints for all parameters simultaneously. The method is universal and easily extensible to other data and collision models.

Investigating the collision energy dependence of

Iurii Karpenko INFN Sezione di Firenze, I-50019 Sesto Fiorentino, Firenze, Italy Abstract We determine the probability distributions of shear viscosity over the entropy density ratio η/s in Au+Au collisions at √ sNN = 19.6, 39, and 62.4 GeV, using Bayesian inference and Gaussian process emulators for a model-to-data statistical analysis that probes the full input parameter space of a transport+viscous hydrodynamics hybrid model. We find the most likely value of η/s to be larger at smaller √ sNN , although the uncertainties still allow for a constant value between 0.10 and 0.15 for the investigated collision energy range.

\eta

The modification of hard jets in an extended static medium held at a fixed temperature is studied using three different Monte-Carlo event generators (LBT, MATTER, MARTINI). Each event generator contains a different set of assumptions regarding the energy and virtuality of the partons within a jet versus the energy scale of the medium, and hence, applies to a different epoch in the space-time history of the jet evolution. For the first time, modeling is developed where a jet may sequentially transition from one generator to the next, on a parton-by-parton level, providing a detailed simulation of the space-time evolution of medium modified jets over a much broader dynamic range than has been attempted previously in a single calculation. Comparisons are carried out for different observables sensitive to jet quenching, including the parton fragmentation function and the azimuthal distribution of jet energy around the jet axis. The effect of varying the boundary between different generators is studied and a theoretically motivated criterion for the location of this boundary is proposed. The importance of such an approach with coupled generators to the modeling of jet quenching is discussed.

/s in the beam energy scan at the BNL Relativistic Heavy Ion Collider using Bayesian statistics

Abstract We study the effects of nucleon substructure on bulk observables in proton-lead collisions at the LHC using Bayesian methodology. Substructure is added to the TRENTo parametric initial condition model using Gaussian nucleons with a variable number of Gaussian partons. We vary the number and width of these partons while recovering the desired inelastic proton-proton cross section and ensemble averaged proton density. We then run the model through a large number of minimum bias hydrodynamic simulations and measure the response of final particle production and azimuthal particle correlations to initial state properties. Once these response functions are determined, we calibrate free parameters of the model using established Bayesian methodology. We comment on the implied viability of the partonic model for describing hydrodynamic behavior in small systems.

Multistage Monte-Carlo simulation of jet modification in a static medium

Abstract We investigate the collision energy dependence of η / s in a transport + viscous hydrodynamics hybrid model. A Bayesian analysis is performed on RHIC beam energy scan data for Au + Au collisions at s NN = 19.6 , 39, and 62.4 GeV. The resulting posterior probability distributions for the model parameters show a preference for a larger value of η / s at 19.6 GeV compared to 62.4 GeV, indicating dependence on baryon chemical potential μ B .

Flow in small and large quark-gluon plasma droplets: the role of nucleon substructure

We present the latest results on the collision energy dependence of