Deep learning extracts a unified in-medium heavy quark potential from multi-energy bottomonium data, finding the real part close to vacuum Cornell form with weak screening while the imaginary part dominates suppression.
Applying Bayesian parameter estimation to relativistic heavy-ion collisions: simultaneous characterization of the initial state and quark-gluon plasma medium
10 Pith papers cite this work. Polarity classification is still indexing.
10
Pith papers citing it
abstract
We quantitatively estimate properties of the quark-gluon plasma created in ultra-relativistic heavy-ion collisions utilizing Bayesian statistics and a multi-parameter model-to-data comparison. The study is performed using a recently developed parametric initial condition model, TRENTO, which interpolates among a general class of particle production schemes, and a modern hybrid model which couples viscous hydrodynamics to a hadronic cascade. We calibrate the model to multiplicity, transverse momentum, and flow data and report constraints on the parametrized initial conditions and the temperature-dependent transport coefficients of the quark-gluon plasma. We show that initial entropy deposition is consistent with a saturation-based picture, extract a relation between the minimum value and slope of the temperature-dependent specific shear viscosity, and find a clear signal for a nonzero bulk viscosity.
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citation-polarity summary
representative citing papers
A conditional flow-matching model trained on CoLBT-hydro reproduces marginal γ-jet medium-response hadron spectra in 0–10% Pb+Pb at 5.02 TeV with ~10⁶× speedup while preserving front and diffusion-wake statistics.
Sequential hadronization predicts v2(D0) > v2(Ds+) from extra partonic flow accumulated by D0 parents between 1.2 Tc and Tc, with universal linear scaling of the hadronic splitting across O-O and Pb-Pb systems.
Factorized sequential Bayesian updates on correlated pseudo-data produce systematic deviations from the joint posterior that increase with correlation strength, while exact conditional-likelihood updates match the joint result; an information decomposition attributes the mismatch to parameter-tuned,
The work presents a dispersive fit for the refractive index of liquid argon incorporating anomalous dispersion and proposes jet drift in simulations of heavy-ion collisions as a way to disentangle medium properties from energy loss.
Mean transverse momentum fluctuations in baryon-rich matter are driven by energy and baryon density variations, remain robust to baryon diffusion, and show splitting between protons and antiprotons.
Volume variation with multiplicity in ultracentral collisions is small when total entropy scales with nuclear mass number, as shown by relating it to initial density fluctuation profiles.
Global analysis of HERA DIS and ALICE Pb+Pb data using saturation initial-state model determines early-time non-equilibrium η/s of QGP.
Bayesian posteriors from JETSCAPE jet-quenching model are largely compatible across centrality but exhibit shifts across beam energy and observable class, with varying ability to predict complementary datasets.
The MUSES Calliope engine computes multi-dimensional QCD equations of state, merges them consistently, and feeds them into viscous hydrodynamic simulations of heavy-ion collisions with movable critical points and critical scaling in transport coefficients.
citing papers explorer
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Unified Extraction of In-Medium Heavy Quark Potentials from RHIC to LHC Energies via Deep Learning
Deep learning extracts a unified in-medium heavy quark potential from multi-energy bottomonium data, finding the real part close to vacuum Cornell form with weak screening while the imaginary part dominates suppression.
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Study of jet-induced hydro response in high-energy heavy-ion collisions with a flow-matching generative model
A conditional flow-matching model trained on CoLBT-hydro reproduces marginal γ-jet medium-response hadron spectra in 0–10% Pb+Pb at 5.02 TeV with ~10⁶× speedup while preserving front and diffusion-wake statistics.
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System-size dependence of the $D^0$--$D_s^+$ flow splitting from early $D_s^+$ formation at $\sqrt{s_{NN}} = 5.36$~TeV
Sequential hadronization predicts v2(D0) > v2(Ds+) from extra partonic flow accumulated by D0 parents between 1.2 Tc and Tc, with universal linear scaling of the hadronic splitting across O-O and Pb-Pb systems.
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Sequential Bayesian inference with correlated heavy-ion datasets
Factorized sequential Bayesian updates on correlated pseudo-data produce systematic deviations from the joint posterior that increase with correlation strength, while exact conditional-likelihood updates match the joint result; an information decomposition attributes the mismatch to parameter-tuned,
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Medium Characterization with Hard Probes: From Cherenkov Light in QED to Jet Drift in QCD
The work presents a dispersive fit for the refractive index of liquid argon incorporating anomalous dispersion and proposes jet drift in simulations of heavy-ion collisions as a way to disentangle medium properties from energy loss.
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Rapidity dependence of mean transverse momentum fluctuation and decorrelation in baryon-dense medium
Mean transverse momentum fluctuations in baryon-rich matter are driven by energy and baryon density variations, remain robust to baryon diffusion, and show splitting between protons and antiprotons.
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The size of the quark-gluon plasma in ultracentral collisions: impact of initial density fluctuations on the average transverse momentum
Volume variation with multiplicity in ultracentral collisions is small when total entropy scales with nuclear mass number, as shown by relating it to initial density fluctuation profiles.
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Constraining hot and cold nuclear matter properties from heavy-ion collisions and deep-inelastic scattering
Global analysis of HERA DIS and ALICE Pb+Pb data using saturation initial-state model determines early-time non-equilibrium η/s of QGP.
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Bayesian inference constraints on jet quenching across centrality, beam energy, and observable classes in LHC heavy-ion collisions
Bayesian posteriors from JETSCAPE jet-quenching model are largely compatible across centrality but exhibit shifts across beam energy and observable class, with varying ability to predict complementary datasets.
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Studying the QCD Matter produced in Heavy-Ion Collisions using the MUSES Calculation Engine
The MUSES Calliope engine computes multi-dimensional QCD equations of state, merges them consistently, and feeds them into viscous hydrodynamic simulations of heavy-ion collisions with movable critical points and critical scaling in transport coefficients.