Phenomenological Constraints on the Transport Properties of QCD Matter with Data-Driven Model Averaging

Everett, D.; Ke, Weiyao; Paquet, Jean-François; Vujanovic, Gojko; Bass, Steffen A.; Du, Lingyan; Gale, Charles; Heffernan, M.; Heinz, Ulrich; Liyanage, D.; Luzum, Matthew; Majumder, Abhijit; McNelis, M.; Shen, Chun; Xu, Y.; Angerami, A.; Cao, Shanshan; Chen, Y.; Coleman, J. P.; Cunqueiro, L.; Dai, T.; Ehlers, R. J.; Elfner, Hannah; Fan, Wenkai; Fries, Rainer J.; Garza, F.; He, Yang; Jacak, B. V.; Jacobs, P. M.; Jeon, Sangyong; Kim, B.; Kordell, M.; Kumar, Amit; Mak, Simon; Mulligan, J. D.; Nattrass, C.; Oliinychenko, Dmytro; Park, C.; Putschke, J.; Roland, G.; Schenke, Bjoern; Schwiebert, Loren; Silva, A.; Sirimanna, C.; Soltz, R. A.; Tachibana, Y.; Wang, X.-N.; Wolpert, Robert L.

doi:10.1103/physrevlett.126.242301

Cited by 119 publications

(108 citation statements)

References 66 publications

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“…Bayesian inference has been utilized previously to extract parameters of the QGP from heavy-ion collision data, in particular the specific shear viscosity η/s [82][83][84][85] and the equation of state (EoS) [86], with the latter result agreeing well with lattice QCD calculations. See also [87].…”

Section: Introductionmentioning

confidence: 88%

Determining the jet transport coefficient q̂ from inclusive hadron suppression measurements using Bayesian parameter estimation

Cao¹,

Chen²,

Coleman³

et al. 2021

Phys. Rev. C

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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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Section: Introductionmentioning

confidence: 88%

Determining the jet transport coefficient q̂ from inclusive hadron suppression measurements using Bayesian parameter estimation

Cao¹,

Chen²,

Coleman³

et al. 2021

Phys. Rev. C

Self Cite

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“…To apply and test transfer learning techniques in this setting, we use a very large set of full-model simulation data that were generated for calibrating the JETSCAPE modeling framework [14], including the following systems: All these simulations share the same set of 17 model parameters described in [11,14]. For model calibration, full-model simulations were performed at 500 design points that uniformly cover the 17-dimensional parameter space within a finite 17-dimensional cube described in [11,14], using maximin Latin Hypercube sampling [66]. 5 For each design point and each particlization model, 2500 simulations were performed with stochastically fluctuating initial conditions and particlization results.…”

Section: A Multistage Model Of Relativistic Heavy Ion Collision Simul...mentioning

confidence: 99%

“…Mixing-andmatching these modules leads to a plethora of theoretical models that, in principle, could all be used to simulate the collision. As recently shown using Bayesian Model Averaging [11], this ambiguity in the theoretical framework can add a significant model uncertainty in the parameter inference. But accounting for it systematically requires studying multiple models, and this generates a need for efficient emulators describing the predictions from different but typically closely related evolution models.…”

Section: Introductionmentioning

confidence: 97%

“…Bayesian inference or Bayesian parameter estimation is a modern statistical method that provides a way to reliably infer the properties of QGP, by accounting methodically for both theoretical and experimental uncertainties. Tremendous progress has been made in the study of relativistic heavy ion collisions over the past decade by providing increasingly reliable constraints and error estimates for the properties of QGP using Bayesian statistical techniques [3][4][5][6][7][8][9][10][11][12][13][14]. As both the model and data have uncertainties, comparing them results in a probability distribution for the model parameters, specifying the probability for a model with a given set of parameters to provide predictions that agree with the experimental observations.…”

Section: Introductionmentioning

confidence: 99%

“…Emulators are machine learning models that provide a computationally efficient prediction of the simulator over the parameter space when trained on a sparse set of full simulation data. While a modeler can choose from a wide range of learning models (e.g., linear regression, decision trees, neural networks) as surrogates for expensive simulations, the standard practice in relativistic nuclear physics [4][5][6][7][8][9][10][11][12][13][14] has been to use Gaussian Process (GP) emulators [17]. There are two reasons for this: (i) GPs provide a flexible non-parametric framework for emulation modeling and (ii) they also provide an efficient quantifica-tion of the predictive uncertainty associated with the interpolation between training points in the n-dimensional parameter space.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient emulation of relativistic heavy ion collisions with transfer learning

Liyanage,

Ji,

Everett

et al. 2022

Preprint

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Measurements from the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) can be used to study the properties of quark-gluon plasma. Systematic constraints on these properties must combine measurements from different collision systems and methodically account for experimental and theoretical uncertainties. Such studies require a vast number of costly numerical simulations. While computationally inexpensive surrogate models ("emulators") can be used to efficiently approximate the predictions of heavy ion simulations across a broad range of model parameters, training a reliable emulator remains a computationally expensive task. We use transfer learning to map the parameter dependencies of one model emulator onto another, leveraging similarities between different simulations of heavy ion collisions. By limiting the need for large numbers of simulations to only one of the emulators, this technique reduces the numerical cost of comprehensive uncertainty quantification when studying multiple collision systems and exploring different models.

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Signature of gluon saturation in forward di-hadron correlations at the Large Hadron Collider

Giacalone

Marquet

2019

Nuclear Physics A

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We argue that gluon saturation effects may be visible in Large Hadron Collider (LHC) data. We look at the centrality dependence of the away-side peak of two-particle correlations measured in p-Pb collisions, using both ALICE data at midrapidity, and LHCb data at forward rapidity. Once the collective flow-like signal measured at the near-side peak is removed from the correlation function, the centrality dependence of the away-side peak turns out to be much stronger in the forward region than at midrapidity. We argue that this is a very specific prediction of the saturation framework, and we compare LHCb data to a state-of-the-art calculation within the Color Glass Condensate theory.

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Phenomenological Constraints on the Transport Properties of QCD Matter with Data-Driven Model Averaging

Cited by 119 publications

References 66 publications

Determining the jet transport coefficient q̂ from inclusive hadron suppression measurements using Bayesian parameter estimation

Determining the jet transport coefficient q̂ from inclusive hadron suppression measurements using Bayesian parameter estimation

Efficient emulation of relativistic heavy ion collisions with transfer learning

Signature of gluon saturation in forward di-hadron correlations at the Large Hadron Collider

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