J. Scott Moreland’s research while affiliated with Duke University and other places

We posit a unified hydrodynamic and microscopic description of the quark-gluon plasma (QGP) produced in ultrarelativistic p−Pb and Pb-Pb collisions at sNN=5.02TeV and evaluate our assertion using Bayesian inference. Specifically, we model the dynamics of both collision systems using initial conditions with parametric nucleon substructure, a preequilibrium free streaming stage, event-by-event viscous hydrodynamics, and a microscopic hadronic afterburner. Free parameters of the model, which describe the initial state and QGP medium are then simultaneously calibrated to fit charged-particle yields, mean pT, and flow cumulants. We argue that the global agreement of the calibrated model with the experimental data strongly supports the existence of hydrodynamic flow in small collision systems at ultrarelativistic energies, and that the flow produced develops at length scales smaller than a single proton. Posterior estimates for the model's input parameters are obtained, and new insights into the temperature dependence of the QGP transport coefficients and event-by-event structure of the proton are discussed.

Ultrarelativistic collisions of heavy atomic nuclei produce an extremely hot and dense phase of matter, known as quark–gluon plasma (QGP), which behaves like a near-perfect fluid with the smallest specific shear viscosity—the ratio of the shear viscosity to the entropy density—of any known substance¹. Due to its transience (lifetime ~ 10⁻²³ s) and microscopic size (10⁻¹⁴ m), the QGP cannot be observed directly, but only through the particles it emits; however, its characteristics can be inferred by matching the output of computational collision models to experimental observations. Previous work, using viscous relativistic hydrodynamics to simulate QGP, has achieved semiquantitative constraints on key physical properties, such as its specific shear and bulk viscosity, but with large, poorly defined uncertainties2–8. Here, we present the most precise estimates so far of QGP properties, including their quantitative uncertainties. By applying established Bayesian parameter estimation methods⁹ to a dynamical collision model and a wide variety of experimental data, we extract estimates of the temperature-dependent specific shear and bulk viscosity simultaneously with related initial-condition properties. The method is extensible to other collision models and experimental data and may be used to characterize additional aspects of high-energy nuclear collisions.

The “unreasonable effectiveness” of relativistic fluid dynamics in describing high energy heavy-ion and even proton-proton collisions will be demonstrated and discussed. Several recent ideas of optimizing relativistic fluid dynamics for the specific challenges posed by such collisions will be presented, and some thoughts will be offered why the framework works better than originally expected. I will also address the unresolved question where exactly hydrodynamics breaks down, and why.

Dynamical models based on relativistic fluid dynamics provide a powerful tool to extract the properties of the strongly-coupled quark-gluon plasma (QGP) produced by ultrarelativistic nuclear collisions. The largest source of uncertainty in these model-to-data extractions is the choice of theoretical initial conditions (ICs) used to model the distribution of energy or entropy at the hydrodynamic starting time. Descriptions of the ICs are generally improved through iterative cycles of testing and refinement. Individual models are compared to experimental data; the worst models are discarded and best models retained. Consequently, successful traits (assumptions) are passed on to subsequent generations of the theoretical landscape. This bottom-up approach correspondingly describes a form of theoretical trial and error, where each trial proposes an ab initio solution to the problem at hand. A natural complement to this strategy, is to employ a top-down or data-driven approach which is able to reverse engineer properties of the ICs from the constraints imposed by the experimental data. In this dissertation, I motivate and develop a parametric IC model based on a family of functions known as the generalized means. The ansatz closely mimics the variability of ab initio calculations and serves as a reasonable parametric form for exploring QGP energy and entropy deposition assuming imperfect knowledge of the complex physical processes which lead to its creation. With the parametric model in hand, I explore broad implications of the proposed ansatz using recently adapted Bayesian methods to simultaneously constrain properties of the ICs and QGP medium using experimental data from the Large Hadron Collider. These analyses show that the ICs are highly constrained by available measurements and provide evidence of a unified hydrodynamic description of small and large nuclear collision systems.

The "unreasonable effectiveness" of relativistic fluid dynamics in describing high energy heavy-ion and even proton-proton collisions are demonstrated and discussed. Several recent ideas of optimizing relativistic fluid dynamics for the specific challenges posed by such collisions will be presented, and some thoughts will be offered why the framework works better than originally expected. I will also address the unresolved question where exactly hydrodynamics breaks down, and why.

We apply a well tested hybrid transport model, which couples viscous hydrodynamics to a hadronic afterburner, to describe bulk observables in proton-lead and lead-lead collisions at sNN=5.02TeV. The quark-gluon plasma (QGP) initial conditions are modeled using the parametric TRENTo model with additional nucleon substructure parameters to vary the number and size of hot spots inside each nucleon, followed by a pre-equilibrium free streaming stage to match the full energy-momentum tensor of the initial state onto viscous hydrodynamics. Initial condition and QGP medium parameters, such as the temperature dependence of the QGP shear and bulk viscosities, are then calibrated using Bayesian parameter estimation to describe charged particle yields, mean pT and anisotropic flow harmonics of both collision systems in a single self-consistent framework. We find that the hybrid model provides a compelling, simultaneous description of both collision systems using appropriately chosen model parameters, and present new posterior estimates for the size and shape of the nucleon and temperature dependence of QGP shear and bulk viscosities.

We posit a unified hydrodynamic and microscopic description of the quark-gluon plasma (QGP) produced in ultrarelativistic p-Pb and Pb-Pb collisions at $\sqrt{s_\mathrm{NN}}=5.02$ TeV and evaluate our assertion using Bayesian inference. Specifically, we model the dynamics of both collision systems using initial conditions with parametric nucleon substructure, a pre-equilibrium free-streaming stage, event-by-event viscous hydrodynamics, and a microscopic hadronic afterburner. Free parameters of the model which describe the initial state and QGP medium are then simultaneously calibrated to fit charged particle yields, mean $p_T$, and flow cumulants. We argue that the global agreement of the calibrated model with the experimental data strongly supports the existence of hydrodynamic flow in small collision systems at ultrarelativistic energies, and that the flow produced develops at length scales smaller than a single proton. Posterior estimates for the model's input parameters are obtained, and new insights into the temperature dependence of the QGP transport coefficients and event-by-event structure of the proton are discussed.

We apply a well tested hybrid transport model, which couples viscous hydrodynamics to a hadronic afterburner, to describe bulk observables in proton-lead and lead-lead collisions at $\sqrt{s_{NN}}=5.02$ TeV. The quark-gluon plasma (QGP) initial conditions are modeled using the parametric TRENTO model with additional nucleon substructure parameters to vary the number and size of hot spots inside each nucleon, followed by a pre-equilibrium free streaming stage to match the full energy-momentum tensor of the initial state onto viscous hydrodynamics. Initial condition and QGP medium parameters, such as the temperature dependence of the QGP shear and bulk viscosities, are then calibrated using Bayesian parameter estimation to describe charged particle yields, mean $p_T$ and anisotropic flow harmonics of both collision systems in a single self-consistent framework. We find that the hybrid model provides a compelling, simultaneous description of both collision systems using appropriately chosen model parameters, and present new posterior estimates for the size and shape of the nucleon and temperature dependence of QGP shear and bulk viscosities.

The initial three-dimensional entropy distribution of the quark-gluon plasma produced in relativistic heavy-ion collision is systematically studied using parametric initial conditions. We apply a cumulant generating function approach to parametrize the rapidity dependence of local entropy deposition and extend the boost-invariant initial condition model TRENTo to include longitudinal structure. Two different parametrizations are tested and their parameters optimized by comparing to centrality dependent charged particle pseudorapidity densities of p+Pb (5.02A TeV) and Pb+Pb (2.76A TeV) using Bayesian inference. Successful parametrizations are then selected based on their ability to describe two-particle psuedorapidity correlations. Finally, we predict the pseudorapidity dependent flows and event-plane decorrelations using the optimized initial conditions and a 3+1D hybrid hydro+micro model.

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 $\eta/s$, 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.