Jean-François Paquet

The JETSCAPE Collaboration reports the first multi-messenger study of the QGP jet transport parameter using Bayesian inference, incorporating all available hadron and jet inclusive yield and jet substructure data from RHIC and the LHC. The theoretical model utilizes virtuality-dependent in-medium partonic energy loss coupled to a detailed dynamical model of QGP evolution. Tension is observed when constraining for different kinematic cuts of the inclusive hadron data. The addition of substructure data is shown to improve the constraint on , without inducing tension with the constraint due to inclusive observables. These studies provide new insight into the mechanisms of jet interactions in matter, and point to next steps in the field for comprehensive understanding of jet quenching as a probe of the QGP.

We utilize event-by-event Monte Carlo simulations within the JETSCAPE framework to examine scale-dependent jet-medium interactions in heavy-ion collisions. The reduction in jet-medium interaction during the early high-virtuality stage, where the medium is resolved at a short distance scale, is emphasized as a key element in explaining multiple jet observables, particularly substructures, simultaneously. By employing the MATTER+LBT setup, which incorporates this explicit reduction of medium effects at high virtuality, we investigate jet substructure observables, such as Soft Drop groomed observables. When contrasted with existing data, our findings spotlight the significant influence of the reduction at the early high-virtuality stages. Furthermore, we study the substructure of gamma-tagged jets, providing predictive insights for future experimental analyses. This broadens our understanding of the various contributing factors involved in modifying jet substructures.

Hybrid Hadronization of Jet Showers from e(++????^‑) to ???? + ???? with JETSCAPE - NASA/ADS Now on home page ads icon ads Enable full ADS view NASA/ADS Hybrid Hadronization of Jet Showers from e(++????^‑) to ???? + ???? with JETSCAPE Angerami, A. ; Arora, R. ; Bass, SA ; Cao, S. ; Chen, Y. ; Ehlers, R. ; Elfner, H. ; Fan, W. ; Fries, RJ ; Gale, C. ; He, Y. ; Heinz, U. ; Jacak, B. ; Jacobs, P. ; Jeon, S. ; Ji, YI ; Kasper, L. ; Kordell, M., II ; Kumar, A. ; Latessa, J. ; Lee, YJ ; Lemmon, R. ; Liyanage, D. ; Lopez, A. ; Luzum, M. ; Majumder, A. ; Mak, S. ; Mankolli, A. ; Martin, C. ; Mehryar, H. ; Mengel, T. ; Mulligan, J. ; Nattrass, C. ; Norman, J. ; Paquet, JF ; Parker, C. ; Putschke, JH ; Roland, G. ; Schenke, B. ; Schwiebert, L. ; Sengupta, A. ; Shen, C. ; Sirimanna, C. ; Soltz, RA ; Soudi, I. ; Strickland, M. ; Tachibana, Y. ; Velkovska, J. ; Vujanovic, G. ; Wang, XN and 2 more Abstract Publication: 11th International Conference on Hard and …

Understanding the modification of jets and high- probes in small systems requires the integration of soft and hard physics. We present recent developments in extending the JETSCAPE framework to build an event generator, which includes correlations between soft and hard partons, to study jet observables in small systems. The multi-scale physics of the collision is separated into different stages. Hard scatterings are first sampled at binary collision positions provided by the Glauber geometry. They are then propagated backward in space-time following an initial-state shower to obtain the initiating partons' energies and momenta before the collision. These energies and momenta are then subtracted from the incoming colliding nucleons for soft-particle production, modeled by the 3D-Glauber + hydrodynamics + hadronic transport framework. This new hybrid approach (X-SCAPE) includes non-trivial correlations between jet and soft particle productions in small systems. We calibrate this framework with the final state hadrons' -spectra from low to high in -, and and then compare with the spectra in - collisions from the LHC. We also present results for additional observables such as the distributions of event activity as a function of the hardest jet in forward and mid-rapidity for both - and - collisions.

Shower development dynamics for a jet traveling through the quark-gluon plasma (QGP) is a multiscale process, where the heavy flavor mass is an important scale. During the high virtuality portion of the jet evolution in the QGP, emission of gluons from a heavy flavor is modified owing to heavy quark mass. Medium-induced radiation of heavy flavor is sensitive to microscopic processes (e.g. diffusion), whose virtuality dependence is phenomenologically explored in this study. In the lower virtuality part of shower evolution, i.e. when the mass is comparable to the virtuality of the parton, scattering and radiation processes of heavy quarks differ from light quarks. The effects of these mechanisms on shower development in heavy flavor tagged showers in the QGP is explored here. Furthermore, this multiscale study examines dynamical pair production of heavy flavor (via virtual gluon splittings) and their subsequent evolution in the QGP, which is not possible otherwise. A realistic event-by-event simulation is performed using the JETSCAPE framework. Energy-momentum exchange with the medium proceeds using a weak coupling recoil approach. Using leading hadron and open heavy flavor observables, differences in heavy versus light quark energy-loss mechanisms are explored, while the importance of heavy flavor pair production is highlighted along with future directions to study.

Prompt photons are created in the early stages of heavy ion collisions and traverse the QGP medium without any interaction. Therefore, photon-triggered jets can be used to study the jet quenching in the QGP medium. In this work, photon-triggered jets are studied through different jet and jet substructure observables for different collision systems and energies using the JETSCAPE framework. Since the multistage evolution used in the JETSCAPE framework is adequate to describe a wide range of experimental observables simultaneously using the same parameter tune, we use the same parameters tuned for jet and leading hadron studies. The same isolation criteria used in the experimental analysis are used to identify prompt photons for better comparison. For the first time, high-accuracy JETSCAPE results are compared with multi-energy LHC and RHIC measurements to better understand the deviations observed in prior studies. This study highlights the importance of multistage evolution for the simultaneous description of experimental observables through different collision systems and energies using a single parameter tune.

This White Paper presents an overview of the current status and future perspective of QCD research, based on the community inputs and scientific conclusions from the 2022 Hot and Cold QCD Town Meeting. We present the progress made in the last decade toward a deep understanding of both the fundamental structure of the sub-atomic matter of nucleon and nucleus in cold QCD, and the hot QCD matter in heavy ion collisions. We identify key questions of QCD research and plausible paths to obtaining answers to those questions in the near future, hence defining priorities of our research over the coming decades.

We extend the well-studied midrapidity TRENTo initial-conditions model to three dimensions, thus facilitating (3+1)D modeling and analysis of ultrarelativistic heavy-ion collisions at RHIC and LHC energies. TRENTo-3D is a fast, parametric model of the 3D initial-state geometry, capable of providing initial conditions for (3+1)D models of quark--gluon plasma formation and evolution. It builds on TRENTo's success at modeling the initial nuclear participant thicknesses, longitudinally extending the initial deposition to form a central fireball near midrapidity and two fragmentation regions at forward and backward rapidities. We validate the new model through a large-scale Bayesian calibration, utilizing as observables the rapidity distributions of charged hadrons. For computational efficiency the present effort employs a (1+1)D linearized approximation of ideal hydrodynamics as a stand-in for quark--gluon plasma dynamics. This calibration serves as model validation and paves the way for utilizing TRENTo-3D as an initial-conditions model for state-of-the-art simulation incorporating (3+1)D relativistic viscous hydrodynamics.

We present predictions and postdictions for a wide variety of hard jet-substructure observables using a multi-stage model within the JETSCAPE framework. The details of the multi-stage model and the various parameter choices are described in [A. Kumar et al., arXiv:2204.01163]. A novel feature of this model is the presence of two stages of jet modification: a high virtuality phase (modeled using MATTER), where coherence effects diminish medium-induced radiation, and a lower virtuality phase (modeled using LBT), where parton splits are fully resolved by the medium as they endure multiple scattering induced energy loss. Energy loss calculations are carried out on event-by-event viscous fluid dynamic backgrounds constrained by experimental data. The uniformed and consistent descriptions of multiple experimental observables demonstrate the essential role of coherence effects and the multi-stage modeling of the jet evolution. Using the best choice of parameters from [A. Kumar et al., arXiv:2204.01163], and with no further tuning, we present calculations for the medium modified jet fragmentation function, the groomed jet momentum fraction and angular separation distributions, as well as the nuclear modification factor of groomed jets. These calculations provide accurate descriptions of published and preliminary data from experiments at RHIC and LHC. Furthermore, we provide predictions from the multi-stage model for future measurements at RHIC.

Hot QCD physics studies the nuclear strong force under extreme temperature and densities. Experimentally these conditions are achieved via high-energy collisions of heavy ions at the Relativistic Heavy Ion Collider (RHIC) and the Large Hadron Collider (LHC). In the past decade, a unique and substantial suite of data was collected at RHIC and the LHC, probing hydrodynamics at the nucleon scale, the temperature dependence of the transport properties of quark-gluon plasma, the phase diagram of nuclear matter, the interaction of quarks and gluons at different scales and much more. This document, as part of the 2023 nuclear science long range planning process, was written to review the progress in hot QCD since the 2015 Long Range Plan for Nuclear Science, as well as highlight the realization of previous recommendations, and present opportunities for the next decade, building on the accomplishments and investments made in theoretical developments and the construction of new detectors. Furthermore, this document provides additional context to support the recommendations voted on at the Joint Hot and Cold QCD Town Hall Meeting, which are reported in a separate document.

Heavy-ion collisions provide a window into the properties of many-body systems of deconfined quarks and gluons. Understanding the collective properties of quarks and gluons is possible by comparing models of heavy-ion collisions to measurements of the distribution of particles produced at the end of the collisions. These model-to-data comparisons are extremely challenging, however, because of the complexity of the models, the large amount of experimental data, and their uncertainties. Bayesian inference provides a rigorous statistical framework to constrain the properties of nuclear matter by systematically comparing models and measurements. This review covers model emulation and Bayesian methods as applied to model-to-data comparisons in heavy-ion collisions. Replacing the model outputs (observables) with Gaussian process emulators is key to the Bayesian approach currently used in the field, and both current uses of emulators and related recent developments are reviewed. The general principles of Bayesian inference are then discussed along with other Bayesian methods, followed by a systematic comparison of seven recent Bayesian analyses that studied quark-gluon plasma properties, such as the shear and bulk viscosities. The latter comparison is used to illustrate sources of differences in analyses, and what it can teach us for future studies.

Considerable information about the early-stage dynamics of heavy-ion collisions is encoded in the rapidity dependence of measurements. To leverage the large amount of experimental data, we perform a systematic analysis using three-dimensional hydrodynamic simulations of multiple collision systems -- large and small, symmetric and asymmetric. Specifically, we perform fully 3D multi-stage hydrodynamic simulations initialized by a parameterized model for rapidity-dependent energy deposition, which we calibrate on the hadron multiplicity and anisotropic flow coefficients. We utilize Bayesian inference to constrain properties of the early- and late- time dynamics of the system, and highlight the impact of enforcing global energy conservation in our 3D model.

Colliding large nuclei at velocities close to the speed of light produces a plasma of strongly interacting nuclear matter known as quark-gluon plasma. This nuclear plasma can be characterized by macroscopic properties such as its equation of state and viscosity. Bayesian inference provides an important tool to constrain systematically these properties of nuclear matter with measurements from the Relativistic Heavy Ion Collider and the Large Hadron Collider. I will survey different use of Bayesian inference in heavy-ion collisions, including applications of transfer learning, model comparison, closure tests, model averaging and stochastic emulator uncertainty optimization.

The Quark-Gluon Plasma (QGP) is a unique phase of nuclear matter, theorized to have filled the Universe shortly after the Big Bang. A critical challenge in studying the QGP is that, to reconcile experimental observables with theoretical parameters, one requires many simulation runs of a complex physics model over a high-dimensional parameter space. Each run is computationally very expensive, requiring thousands of CPU hours, thus limiting physicists to only several hundred runs. Given limited training data for high-dimensional prediction, existing surrogate models often yield poor predictions with high predictive uncertainties, leading to imprecise scientific findings. To address this, we propose a new Additive Multi-Index Gaussian process (AdMIn-GP) model, which leverages a flexible additive structure on low-dimensional embeddings of the parameter space. This is guided by prior scientific knowledge that the QGP is dominated by multiple distinct physical phenomena (i.e., multiphysics), each involving a small number of latent parameters. The AdMIn-GP models for such embedded structures within a flexible Bayesian nonparametric framework, which facilitates efficient model fitting via a carefully constructed variational inference approach with inducing points. We show the effectiveness of the AdMIn-GP via a suite of numerical experiments and our QGP application, where we demonstrate considerably improved surrogate modeling performance over existing models.

We present a new study of jet interactions in the quark-gluon plasma created in high-energy heavy-ion collisions, using a multistage event generator within the jetscape framework. We focus on medium-induced modifications in the rate of inclusive jets and high transverse momentum (high-p T) hadrons. Scattering-induced jet energy loss is calculated in two stages: a high virtuality stage based on the matter model, in which scattering of highly virtual partons modifies the vacuum radiation pattern, and a second stage at lower jet virtuality based on the lbt model, in which leading partons gain and lose virtuality by scattering and radiation. Coherence effects that reduce the medium-induced emission rate in the matter phase are also included. The trent o model is used for initial conditions, and the (2+ 1) dimensional vishnu model is used for viscous hydrodynamic evolution. Jet interactions with the medium are modeled …

I present an overview of photon and dilepton production in heavy-ion collisions, highlighting recent progress and ongoing challenges, with focus on hard initial scatterings, pre-equilibrium electromagnetic emission, as well as thermal and hadronic production. The potential of photons and dileptons to probe low-energy collisions is discussed briefly.

The Gubser solution to inviscid relativistic fluid dynamics is used to examine the role of transverse expansion on the energy spectrum of photons radiated by quark-gluon plasma. Transverse flow is shown to be a modest effect on the energy spectrum of photons as a whole, despite its large effect on rare high-energy photons produced at low temperatures. An exact expression is derived for the volume of the plasma as a function of its temperature. A simple formula is obtained for the energy spectrum of high-energy thermal photons, which is used to relate the inverse slope T eff of the photon spectrum at energy E to the maximum temperature of the plasma T 0, finding T eff≈ T 0/(1+ 5 2 T 0 E).

Bayesian parameter estimation provides a systematic approach to compare heavy-ion collision models with measurements, leading to constraints on the properties of nuclear matter with proper accounting of experimental and theoretical uncertainties. Aside from statistical and systematic model uncertainties, interpolation uncertainties can also play a role in Bayesian inference, if the model's predictions can only be calculated at a limited set of model parameters. This uncertainty originates from using an emulator to interpolate the model's prediction across a continuous space of parameters. In this work, we study the trade-offs between the emulator (interpolation) and statistical uncertainties. We perform the analysis using spatial eccentricities from the T R ENTo model of initial conditions for nuclear collisions. Given a fixed computational budget, we study the optimal compromise between the number of parameter …

It is currently understood that there are four fundamental forces in nature: gravitational, electromagnetic, weak and strong forces. The strong force governs the interactions between quarks and gluons, elementary particles whose interactions give rise to the vast majority of visible mass in the universe. The mathematical description of the strong force is provided by the non-Abelian gauge theory Quantum Chromodynamics (QCD). While QCD is an exquisite theory, constructing the nucleons and nuclei from quarks, and furthermore explaining the behavior of quarks and gluons at all energies, remain to be complex and challenging problems. Such challenges, along with the desire to understand all visible matter at the most fundamental level, position the study of QCD as a central thrust of research in nuclear science. Experimental insight into the strong force can be gained using large particle accelerator facilities, which are necessary to probe the very short distance scales over which quarks and gluons interact. The Long Range Plans (LRPs) exercise of 1989 and 1996 led directly to the construction of two world-class facilities: the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab (JLab) that is focused on studying how the structure of hadrons emerges from QCD (cold QCD research), and the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Lab (BNL) that aims at the discovery and study of a new state of matter, the quark-gluon plasma (QGP), at extremely high temperatures (hot QCD research). The different collision systems used to access the incredibly rich field of hot and cold QCD in the laboratory are illustrated …

We study parton energy-momentum exchange with the quark gluon plasma (QGP) within a multistage approach composed of in-medium DGLAP evolution at high virtuality, and (linearized) Boltzmann Transport formalism at lower virtuality. This multistage simulation is then calibrated in comparison with high charged hadrons, D-mesons, and the inclusive jet nuclear modification factors, using Bayesian model-to-data comparison, to extract the virtuality-dependent transverse momentum broadening transport coefficient . To facilitate this undertaking, we develop a quantitative metric for validating the Bayesian workflow, which is used to analyze the sensitivity of various model parameters to individual observables. The usefulness of this new metric in improving Bayesian model emulation is shown to be highly beneficial for future such analyses.