Transverse Momentum Differential Global Analysis of Heavy-Ion Collisions

Abstract

The understanding of heavy ion collisions and its quark-gluon plasma formation requires a complicated interplay of rich physics in a wealth of experimental data. In this work we compare for identified particles the transverse momentum dependence of both the yields and the anisotropic flow coefficients for both PbPb and pPb collisions. We do this in a global model fit including a free streaming prehydrodynamic phase with variable velocity v fs , thereby widening the scope of initial conditions. During the hydrodynamic phase we vary three second order transport coefficients. The free streaming velocity has a preference slightly below the speed of light. In this extended model the bulk viscosity is small and even consistent with zero.

Inferring nuclear structure from heavy isobar collisions using Trajectum

“…2. The model -In this work we use the publicly available Trajectum 1.2 framework [15][16][17][18] using the maximum likelihood settings and UrQMD for the hadronic gas phase [19,20] as in [17]. Trajectum uses the T R ENTo model [21] to compute the initial state, where the positions of the nucleons within the nucleus are given by a Woods-Saxon (WS) distribution,…”

“…2). This in turn could have effects on β 2 (see appendix B), which indicates that a full global analysis [16,31,32] may be needed to obtain even more accurate results. Importantly, a full hydrodynamic analysis seems necessary as just focusing on the n initial anisotropies does not capture the v n {2} dependence quantitatively (see appendix B).…”

Efficient emulation of relativistic heavy ion collisions with transfer learning

“…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.…”

“…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.…”

Constraining the nucleon size with relativistic nuclear collisions

“…Such nontrivial findings do not have, at present, a clear explanation. Furthermore, analyses of Pb+Pb and also p+Pb data [14][15][16][17] including nucleon constituents with the auxiliary width parameter w q , also return rather diffuse nucleons, w = 0.8-0.9 fm, albeit with w q ≈ 0.4 fm. Hydrodynamic results within all these frameworks, e.g., starting with either IP-Glasma initial conditions with sharp nucleons or JETSCAPE MAP T R ENTo parameters with very diffuse nucleons return similar final-state observables.…”