Bulletin of the American Physical Society
2019 Fall Meeting of the APS Division of Nuclear Physics
Volume 64, Number 12
Monday–Thursday, October 14–17, 2019; Crystal City, Virginia
Session EJ: Mini-symposium: Quantitative Understanding of QGP Properties I |
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Chair: Rainer Fries, Texas A&M Room: Salon C |
Tuesday, October 15, 2019 8:30AM - 9:06AM |
EJ.00001: Extracting medium properties from comparisons of collision models to data Invited Speaker: Steffen Bass A primary goal of heavy-ion physics is the measurement of the fundamental properties of the quark-gluon plasma (QGP), notably its transport coefficients and initial state properties. Since these properties are not directly measurable, one relies on a comparison of experimental data to computational models of the time-evolution of the collision to connect measured observables to the properties of the transient QGP state. These model-to-data comparisons are non-trivial due to the large number of model parameters and the non-factorizing sensitivity of measured observables to multiple parameters. Over the last few years techniques based on Bayesian statistics have been developed that allow for the simultaneous calibration of a large number of model parameters and the precision extraction of QGP properties including their quantified uncertainties. The computational models can take many forms, but need to be governed by parameters that codify the physical properties we wish to extract, for example the temperature and/or momentum dependent transport coefficients. The Bayesian analysis then evaluates the model at a small set of points in the multidimensional parameter space, varying all parameters simultaneously. Gaussian process emulators are used to non-parametrically interpolate the parameter space, providing fast predictions at any point in parameter space with quantitative uncertainty. Finally, the parameter space is systematically explored using a Markov chain Monte Carlo (MCMC) to obtain rigorous constraints on all parameters simultaneously, including all correlations among the parameters. In this talk I will review the basic components of the Bayesian analysis and discuss recent progress in the determination of QGP initial conditions and transport coefficients, including the QGP shear and bulk viscosities and heavy quark transport coefficient. [Preview Abstract] |
Tuesday, October 15, 2019 9:06AM - 9:18AM |
EJ.00002: ABSTRACT WITHDRAWN |
Tuesday, October 15, 2019 9:18AM - 9:30AM |
EJ.00003: Jet Trigger and Jet Reconstruction Performance in Pb+Pb Collisions at ATLAS Wenkai Zou Jets in heavy-ion collisions provide a powerful tool to probe the hot and dense QCD medium created in these collisions. In the ATLAS experiment a set of dedicated heavy ion jet triggers are designed to record the events containing jets in a wide range of transverse energies. Further, a jet reconstruction algorithm optimized to correct for the large event-by-event dependent underlying event produced in heavy ion collisions is used in the offline reconstruction of the data. This talk presents the performance of the jet trigger and offline jet reconstruction used by ATLAS experiment in the 2018 heavy ion run where ATLAS recorded Pb+Pb collisions at the center of mass energy of 5.02 TeV. Trigger and reconstruction efficiencies, jet energy and angular scales and resolutions are presented. The study is performed for both small $R=0.4$ and large $R=1.0$ jets. This study might point to possible improvements for the upcoming heavy ion runs. [Preview Abstract] |
Tuesday, October 15, 2019 9:30AM - 9:42AM |
EJ.00004: First measurements of the jet mass in p+p collisions at $\sqrt{s}=200$ GeV at STAR Isaac Mooney Partonic energy loss in a hot, dense QCD medium may be dependent on the parton's virtuality. In this talk, we present the first measurements of a related observable called the jet invariant mass, $M$, in p+p collisions at $\sqrt{s}=200$ GeV at STAR. We also present the SoftDrop groomed mass, $M_{\mathrm{g}}$, for which the contribution of wide-angle non-perturbative radiation is suppressed, facilitating comparisons with Monte Carlo simulations. The measurements are differential in both the jet transverse momentum, $p_{\mathrm{T}}$, and jet radius parameter, $R$. After fully correcting for detector effects, we compare our jet mass and groomed mass results to leading-order Monte Carlo event generators PYTHIA and HERWIG, which differ both in parton shower and hadronization mechanisms. We find that PYTHIA6 tuned to RHIC kinematics agrees well with the measurement, while the corresponding LHC tunes for PYTHIA8 and HERWIG7 have significant disagreement with the data. Such a comparison presents an opportunity for further tuning of Monte Carlo event generators. Study of the jet mass in p+p collisions will serve as a baseline for future work in p+A and A+A collisions to explore cold and hot nuclear matter effects. [Preview Abstract] |
(Author Not Attending)
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EJ.00005: Jet quenching in a multi-stage approach Amit Kumar In this talk, we present a comprehensive study by performing a model-to-data comparison for leading hadrons, inclusive jets, and jet substructure observables. Using the JETSCAPE framework, we succeed in providing a simultaneous description of the nuclear modification factor for single hadrons and jets, jet shape, and jet fragmentation function within a unified multi-stage framework which spans multiple centralities, energies and jet radii. This multi-scale approach includes a high virtuality (radiation dominated) generator (MATTER), followed by an on-shell energy loss generator (LBT/MARTINI) or a strongly coupled drag energy loss (AdS/CFT) stage. Each stage transitions to the next at a parton-by-parton level, depending on local quantities such as the parton’s energy, virtuality, and the local density. Measurements of jet and single hadron $R_{AA}$ set strong constraints on the phase-space available for each stage of the energy-loss. We also incorporate jet-medium response through a weakly-coupled transport description with recoil particles excited from the QCD medium. We highlight the central role played by recoil in the description of both integrated jet observables and the sub-structure of the jet. [Preview Abstract] |
Tuesday, October 15, 2019 9:54AM - 10:06AM |
EJ.00006: Bayesian extraction of $\hat{q}$ with correlated experimental errors Ron Soltz We use Bayesian inference to constrain four- and five- component parameterized dependence of the jet transport coefficient $\hat{q}$ on the local temperature and the energy and virtuality of a parton scattering off a thermal medium. These parameters differentiate between different types of energy loss mechanisms; their. For the evolution in energy and virtuality of partons propagating through a 2+1D viscous fluid dynamical medium we explore a high virtuality shower simulator (MATTER), an on-shell transport simulator (LBT), and a combination of the two. All simulations are carried out within the multi-stage JETSCAPE framework. To minimize sensitivity to recoil effects, we focus on single hadron suppression at multiple collision energies and centralities. Previous analyses of relativistic heavy-ion data have neglected correlations among the experimental errors because typically the full error covariance matrix is not available. In this work we introduce as part of the experimental error treatment a correlation length for the systematic errors, for which we explore different ansatze. Our finding underscores the importance of reporting full error covariance matrices for the experimental data. [Preview Abstract] |
Tuesday, October 15, 2019 10:06AM - 10:18AM |
EJ.00007: Machine Learning Based Jet $p_{\rm T}$ Reconstruction with Full Jets in ALICE Hannah Bossi Reconstructing the jet transverse momentum ($p_{\rm T}$) is a challenging task, particularly in heavy ion collisions due to the large fluctuating background from the underlying event. One common treatment of this background is to subtract the event-averaged momentum density (excluding the two leading jets) multiplied by the jet area from the original jet transverse momentum. While this method effectively corrects for the average background, it does not account for region-to-region fluctuations. A novel method to correct the jet transverse momentum on a jet-by-jet basis to reduce these fluctuations will be presented. We utilize machine learning techniques to predict the background-free detector-level jet $p_{\rm T}$ from jet parameters, including the constituents of the jet. The performance of this approach is evaluated using jets from PYTHIA simulations embedded into ALICE Pb--Pb data. In comparison to the standard area-based method, these machine learning based estimators show a significantly improved performance, which could allow for measurements of jets to lower transverse momenta and larger jet radii. [Preview Abstract] |
Tuesday, October 15, 2019 10:18AM - 10:30AM |
EJ.00008: Soft and Collective Particle Generator for a Better Understanding of Heavy Ion Background in Jet Studies Charles Hughes, Alex Aukerman, Thomas Krobatsch, Adam Matyja, Christine Nattrass, James Neuhaus, William Witt Collisions of atomic nuclei moving near the speed of light at the Large Hadron Collider (LHC) generate the Quark Gluon Plasma (QGP), a novel phase of nuclear matter. Jets generated early in the nuclear collision, when internal quarks and gluons scatter with high momentum transfer, provide a tool for studying the properties of the QGP. These quarks and gluons traverse the QGP as it forms, lose energy, and become collimated streams of hadrons. The main difficulty in measurements of jet properties is the large background of hadrons due to the multitude of soft collisions from the expansion and cooling of the short lived QGP. We generate a data-driven background for jets based on measurements of hadron transverse momentum spectra and hadron azimuthal flow at the LHC. We use this data-driven background in concert with Monte-Carlo parton shower generators and jet-finding algorithms to better understand how lower momentum jets are modified. We present the current status of these studies. [Preview Abstract] |
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