We study the properties of the strongly-coupled quark-gluon plasma with a multistage model of heavy ion collisions that combines the TRENTo initial condition ansatz, free-streaming, viscous relativistic hydrodynamics, and a relativistic hadronic transport. A model-to-data comparison with Bayesian inference is performed, revisiting assumptions made in previous studies. The role of parameter priors is studied in light of their importance towards the interpretation of results. We emphasize the use of closure tests to perform extensive validation of the analysis workflow before comparison with observations. Our study combines measurements from the Large Hadron Collider and the Relativistic Heavy Ion Collider, achieving a good simultaneous description of a wide range of hadronic observables from both colliders. The selected experimental data provide reasonable constraints on the shear and the bulk viscosities of the quark-gluon plasma at T ∼ 150-250 MeV, but their constraining power degrades at higher temperatures T 250 MeV. Furthermore, these viscosity constraints are found to depend significantly on how viscous corrections are handled in the transition from hydrodynamics to the hadronic transport. Several other model parameters, including the free-streaming time, show similar model sensitivity, while the initial condition parameters associated with the TRENTo ansatz are quite robust against variations of the particlization prescription. We also report on the sensitivity of individual observables to the various model parameters. Finally, Bayesian model selection is used to quantitatively compare the agreement with measurements for different sets of model assumptions, including different particlization models and different choices for which parameters are allowed to vary between RHIC and LHC energies. CONTENTS Pratt-Torrieri-Bernhard 10 D. Hadronic transport 11 IV. Specifying prior knowledge 11 V. Bayesian Parameter Estimation with a Statistical Emulator 13 A. Overview of Bayesian Parameter Estimation 13 B. Physical model emulator 14 C. Treatment of uncertainties 16 D. Sampling of the posterior 17 E. Maximizing the posterior 17 VI. Closure Tests 17 A. Validating Bayesian inference with closure tests 18 B. Guiding analyses with closure tests 18 37 A. Full posterior of model parameters 37 B. Posterior for LHC and RHIC independently 37 C. Validation of principal component analysis 37 D. Experimental covariance matrix 38 E. Reducing experimental uncertainty 39 F. Bulk relaxation time 39 G. Comparison to previous studies 40 1. Physics models 41 2. Prior distributions 42 3. Experimental data 42 H. Multistage model validation 42 1. Validation of second-order viscous hydrodynamics implementation 42 a. Validation against cylindrically symmetric external solution 43 2. SMASH 43 3. Comparison of JETSCAPE with hic-eventgen 45 4. The σ meson 46 5. Sampling particles on mass-shell 47 6. QCD equations of state with different hadron resonance gases 47 References 48
This paper introduces a new way to compact a continuous probability distribution F into a set of representative points called support points. These points are obtained by minimizing the energy distance, a statistical potential measure initially proposed by Székely and Rizzo [InterStat 5 (2004) 1-6] for testing goodness-of-fit. The energy distance has two appealing features. First, its distance-based structure allows us to exploit the duality between powers of the Euclidean distance and its Fourier transform for theoretical analysis. Using this duality, we show that support points converge in distribution to F , and enjoy an improved error rate to Monte Carlo for integrating a large class of functions. Second, the minimization of the energy distance can be formulated as a difference-of-convex program, which we manipulate using two algorithms to efficiently generate representative point sets. In simulation studies, support points provide improved integration performance to both Monte Carlo and a specific quasi-Monte Carlo method. Two important applications of support points are then highlighted: (a) as a way to quantify the propagation of uncertainty in expensive simulations and (b) as a method to optimally compact Markov chain Monte Carlo (MCMC) samples in Bayesian computation.
In the quest for advanced propulsion and power-generation systems, high-fidelity simulations are too computationally expensive to survey the desired design space, and a new design methodology is needed that combines engineering physics, computer simulations and statistical modeling. In this paper, we propose a new surrogate model that provides efficient prediction and uncertainty quantification of turbulent flows in swirl injectors with varying geometries, devices commonly used in many engineering applications. The novelty of the proposed method lies in the incorporation of known physical properties of the fluid flow as simplifying assumptions for the statistical model. In view of the massive simulation data at hand, which is on the order of hundreds of gigabytes, these assumptions allow for accurate flow predictions in around an hour of computation time. To contrast, existing flow emulators which forgo such simplications may require more computation time for training and prediction than is needed for conducting the simulation itself. Moreover, by accounting for coupling mechanisms between flow variables, the proposed model can jointly reduce prediction uncertainty and extract useful flow physics, which can then be used to guide further investigations.
We report a new determination of qˆ, the jet transport coefficient of the quark-gluon plasma. We use the JETSCAPE framework, which incorporates a novel multistage theoretical approach to in-medium jet evolution and Bayesian inference for parameter extraction. The calculations, based on the MATTER and LBT jet quenching models, are compared to experimental measurements of inclusive hadron suppression in Au + Au collisions at the BNL Relativistic Heavy Ion Collider (RHIC) and Pb + Pb collisions at the CERN Large Hadron Collider (LHC). The correlation of experimental systematic uncertainties is accounted for in the parameter extraction. The functional dependence of qˆ on jet energy or virtuality and medium temperature is based on a perturbative picture of in-medium scattering, with components reflecting the different regimes of applicability of MATTER and LBT. In the multistage approach, the switch between MATTER and LBT is governed by a virtuality scale Q 0 .Comparison of the posterior model predictions to the RHIC and LHC hadron suppression data shows reasonable agreement, with moderate tension in limited regions of phase space. The distribution of qˆ/T 3 extracted from the posterior distributions exhibits weak dependence on jet momentum and medium temperature T , with 90% credible region (CR) depending on the specific choice of model configuration. The choice of MATTER+LBT, with switching at virtuality Q 0 , has 90% CR of 2 < qˆ/T 3 < 4for p T,jet > 40 GeV/c. The value of Q 0 , determined here for the first time, is in the range 2.0-2.7 GeV.
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