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The polarization of Λ and Λ¯ hyperons along the beam direction has been measured relative to the second and third harmonic event planes in isobar Ru+Ru and Zr+Zr collisions at √sNN = 200 GeV. This is the first experimental evidence of the hyperon polarization by the triangular flow originating from the initial density fluctuations. The amplitudes of the sine modulation for the second and third harmonic results are comparable in magnitude, increase from central to peripheral collisions, and show a mild pT dependence. The azimuthal angle dependence of the polarization follows the vorticity pattern expected due to elliptic and triangular anisotropic flow, and qualitatively disagree with most hydrodynamic model calculations based on thermal vorticity and shear induced contributions. The model results based on one of existing implementations of the shear contribution lead to a correct azimuthal angle dependence, but predict centrality and pT dependence that still disagree with experimental measurements. Thus, our results provide stringent constraints on the thermal vorticity and shear-induced contributions to hyperon polarization. Comparison to previous measurements at RHIC and the LHC for the second-order harmonic results shows little dependence on the collision system size and collision energy.
J/ψ suppression has long been considered a sensitive signature of the formation of the Quark-Gluon Plasma (QGP) in relativistic heavy-ion collisions. In this letter, we present the first measurement of inclusive J/ψ production at mid-rapidity through the dimuon decay channel in Au+Au collisions at √sNN = 200 GeV with the STAR experiment. These measurements became possible after the installation of the Muon Telescope Detector was completed in 2014. The J/ψ yields are measured in a wide transverse momentum (pT) range of 0.15 GeV/c to 12 GeV/c from central to peripheral collisions. They extend the kinematic reach of previous measurements at RHIC with improved precision. In the 0-10% most central collisions, the J/ψ yield is suppressed by a factor of approximately 3 for pT > 5 GeV/c relative to that in p + p collisions scaled by the number of binary nucleon-nucleon collisions. The J/ψ nuclear modification factor displays little dependence on pT in all centrality bins. Model calculations can qualitatively describe the data, providing further evidence for the color-screening effect experienced by J/ψ mesons in the QGP.
We present the results of charged particle fluctuations measurements in Au+Au collisions at sqrt[sNN ]=130 GeV using the STAR detector. Dynamical fluctuations measurements are presented for inclusive charged particle multiplicities as well as for identified charged pions, kaons, and protons. The net charge dynamical fluctuations are found to be large and negative providing clear evidence that positive and negative charged particle production is correlated within the pseudorapidity range investigated. Correlations are smaller than expected based on model-dependent predictions for a resonance gas or a quark-gluon gas which undergoes fast hadronization and freeze-out. Qualitative agreement is found with comparable scaled p+p measurements and a heavy ion jet interaction generation model calculation based on independent particle collisions, although a small deviation from the 1/N scaling dependence expected from this model is observed.
We present STAR measurements of charged hadron production as a function of centrality in Au+Au collisions at sqrt[sNN ]=130 GeV . The measurements cover a phase space region of 0.2< pT <6.0 GeV/c in transverse momentum and -1< eta <1 in pseudorapidity. Inclusive transverse momentum distributions of charged hadrons in the pseudorapidity region 0.5< | eta | <1 are reported and compared to our previously published results for | eta | <0.5 . No significant difference is seen for inclusive pT distributions of charged hadrons in these two pseudorapidity bins. We measured dN/d eta distributions and truncated mean pT in a region of pT > pcutT , and studied the results in the framework of participant and binary scaling. No clear evidence is observed for participant scaling of charged hadron yield in the measured pT region. The relative importance of hard scattering processes is investigated through binary scaling fraction of particle production.
A primordial state of matter consisting of free quarks and gluons that existed in the early universe a few microseconds after the Big Bang is also expected to form in high-energy heavy-ion collisions. Determining the equation of state (EoS) of such a primordial matter is the ultimate goal of high-energy heavy-ion experiments. Here we use supervised learning with a deep convolutional neural network to identify the EoS employed in the relativistic hydrodynamic simulations of heavy ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering the nature of the phase transition in quantum chromodynamics. Such EoS-meter is model-independent and insensitive to other simulation inputs including the initial conditions for hydrodynamic simulations.
In this proceeding we review our recent work using supervised learning with a deep convolutional neural network (CNN) to identify the QCD equation of state (EoS) employed in hydrodynamic modeling of heavy-ion collisions given only final-state particle spectra ρ(pT, Ф). We showed that there is a traceable encoder of the dynamical information from phase structure (EoS) that survives the evolution and exists in the final snapshot, which enables the trained CNN to act as an effective “EoS-meter” in detecting the nature of the QCD transition.
Dihadron angular correlations in d + Au collisions at √sNN = 200 GeV are reported as a function of the measured zero-degree calorimeter neutral energy and the forward charged hadron multiplicity in the Au-beam direction. A finite correlated yield is observed at large relative pseudorapidity (η) on the near side (i.e. relative azimuth φ ∼ 0). This correlated yield as a function of η appears to scale with the dominant, primarily jet-related, away-side (φ ∼ π) yield. The Fourier coefficients of the φ correlation, Vn = (cosnφ), have a strong η dependence. In addition, it is found that V1 is approximately inversely proportional to the mid-rapidity event multiplicity, while V2 is independent of it with similar magnitude in the forward (d-going) and backward (Au-going) directions.
The state-of-the-art pattern recognition method in machine learning (deep convolution neural network) is used to identify the equation of state (EoS) employed in the relativistic hydrodynamic simulations of heavy ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering the nature of the phase transition in QCD. The EoS-meter is model independent and insensitive to other simulation inputs including the initial conditions and shear viscosity for hydrodynamic simulations. Through this study we demonstrate that there is a traceable encoder of the dynamical information from the phase structure that survives the evolution and exists in the final snapshot of heavy ion collisions and one can exclusively and effectively decode these information from the highly complex final output with machine learning when traditional methods fail. Besides the deep neural network, the performance of traditional machine learning classifiers are also provided.
In this proceeding, we review our recent work using deep convolutional neural network (CNN) to identify the nature of the QCD transition in a hybrid modeling of heavy-ion collisions. Within this hybrid model, a viscous hydrodynamic model is coupled with a hadronic cascade “after-burner”. As a binary classification setup, we employ two different types of equations of state (EoS) of the hot medium in the hydrodynamic evolution. The resulting final-state pion spectra in the transverse momentum and azimuthal angle plane are fed to the neural network as the input data in order to distinguish different EoS. To probe the effects of the fluctuations in the event-by-event spectra, we explore different scenarios for the input data and make a comparison in a systematic way. We observe a clear hierarchy in the predictive power when the network is fed with the event-by-event, cascade-coarse-grained and event-fine-averaged spectra. The carefully-trained neural network can extract high-level features from pion spectra to identify the nature of the QCD transition in a realistic simulation scenario.