Jiangyong Jia’s research while affiliated with Stony Brook University and other places

Radial flow is a key collective phenomenon in heavy-ion collisions, manifests through event-by-event fluctuations in transverse momentum ($p_{\mathrm{T}}$) spectra. The $p_{\mathrm{T}}$-differential radial flow, $v_0(p_{\mathrm{T}})$, initially conceived to capture local spectral shape fluctuations, is influenced by global multiplicity fluctuations. Using the HIJING model, we explore how different definitions of event activity for centrality and spectral normalization schemes affect $v_0(p_{\mathrm{T}})$. We find these methodological variations induce a constant offset in $v_0(p_{\mathrm{T}})$ without altering its shape, indicating that the dynamic $p_{\mathrm{T}}$-differential information on radial flow remains robust, but its absolute magnitude is meaningful only up to a baseline offset dictated by global multiplicity fluctuations.

Some atomic nuclei exhibit ``pear" shapes arising from octupole deformation ($\beta_3$), though direct experimental evidences for such exotic shapes remains scarce. Low-energy model studies suggest $^{238}$U may have a modest octupole deformation arising from collective vibrational degrees of freedom, in addition to a large prolate shape. We investigated the impact of this modest octupole shape on observables involving triangular flow ($v_3$) in high-energy nuclear collisions. Using a hydrodynamic framework, we show $v_3$ and its correlation with mean transverse momentum, \langle v_3^2 \delta\pT \rangle, exhibit strong sensitivity to $\beta_3$. We found that $\langle v_3^2\rangle$ follows a linear increase with $\beta_3^2$, while \langle v_3^2 \delta\pT \rangle is suppressed in the presence of $\beta_3$. Our findings show that the collective-flow-assisted nuclear imaging method in high-energy nuclear collisions, when compared with experimental data, can provide unique constraints on higher-order deformations.

Can relativistic heavy-ion collisions only probe the global shape of colliding nuclei, or their detailed internal structure as well? Taking $^{20}$Ne as an example, we attempt to directly probe its internal $\alpha$-cluster structure, by comparing experimentally measured observables in collisions at relativistic energies from density distributions of $^{20}$Ne with and without $\alpha$-cluster structure. Since the two density distributions give the same nucleus size and deformation, they lead to similar mid-rapidity observables. However, the $\alpha$-cluster structure may considerably reduce the free spectator nucleon yield and enhance the spectator light nuclei yield, as a result of more compact initial phase-space distribution of nucleons inside $\alpha$ clusters. We propose to measure the scaled yield ratio of free spectator neutrons to charged particles with mass-to-charge ratio $A/Z = 3$, 3/2, and 2 in ultra-central \nene\ collisions, which is found to be reduced by about $25\%$ at $\sqrt{s_\mathrm{NN}} = 7$ TeV and about 20\% at $\sqrt{s_\mathrm{NN}} = 200$ GeV with $\alpha$-cluster structure in $^{20}$Ne. This scaled yield ratio thus serves as a robust and direct probe of the existence of $\alpha$-cluster structure in $^{20}$Ne free from the uncertainty of mid-rapidity dynamics.

Machine learning offers a powerful framework for validating and predicting atomic mass. We compare three improved neural network methods for representation and extrapolation for atomic mass prediction. The powerful method, adopting a macroscopic-microscopic approach and treating complex nuclear effects as output labels, achieves superior accuracy in AME2020, yielding a much lower root-mean-square deviation of 0.122 MeV in the test set, significantly lower than alternative methods. It also exhibits a better extrapolation performance when predicting AME2020 from AME2016, with a root-mean-square deviation of 0.191 MeV. We further conduct sensitivity analyses against the model inputs to verify interpretable alignment beyond statistical metrics. Incorporating theoretical predictions of magic numbers and masses, our fully connected neural networks reproduce key nuclear phenomena including nucleon pairing correlation and magic number effects. The extrapolation capability of the framework is discussed and the accuracy of predicting new mass measurements for isotope chains has also been tested.

High-energy nuclear collisions encompass three key stages: the structure of the colliding nuclei, informed by low-energy nuclear physics, the initial condition , leading to the formation of quark–gluon plasma (QGP), and the hydrodynamic expansion and hadronization of the QGP, leading to final-state hadron distributions that are observed experimentally. Recent advances in both experimental and theoretical methods have ushered in a precision era of heavy-ion collisions, enabling an increasingly accurate understanding of these stages. However, most approaches involve simultaneously determining both QGP properties and initial conditions from a single collision system, creating complexity due to the coupled contributions of these stages to the final-state observables. To avoid this, we propose leveraging established knowledge of low-energy nuclear structures and hydrodynamic observables to independently constrain the QGP’s initial condition. By conducting comparative studies of collisions involving isobar-like nuclei—species with similar mass numbers but different ground-state geometries—we can disentangle the initial condition’s impacts from the QGP properties. This approach not only refines our understanding of the initial stages of the collisions but also turns high-energy nuclear experiments into a precision tool for imaging nuclear structures, offering insights that complement traditional low-energy approaches. Opportunities for carrying out such comparative experiments at the Large Hadron Collider and other facilities could significantly advance both high-energy and low-energy nuclear physics. Additionally, this approach has implications for the future electron-ion collider. While the possibilities are extensive, we focus on selected proposals that could benefit both the high-energy and low-energy nuclear physics communities. Originally prepared as input for the long-range plan of U.S. nuclear physics, this white paper reflects the status as of September 2022, with a brief update on developments since then.

Correlations between event-by-event fluctuations in the amplitudes of flow harmonics offer a novel way to access initial state properties in heavy-ion collisions. We have extensively predicted correlations in different flow harmonics based on multiparticle cumulants in $^{96}$Ru+$^{96}$Ru and $^{96}$Zr+$^{96}$Zr collisions at $\sqrt{s_{NN}} =$ 200 GeV from a multiphase transport model. The state-of-the-art correlated nuclear distributions for the isobars were used to show the difference in nuclear deformations and neutron skin thickness, which have distinct characteristics seen in multiparticle azimuthal correlation. We also found a minimal effect of the shear viscosity effect on these multiparticle azimuthal correlations. Therefore, these studies could also serve as an additional tool for understanding the nature of the initial state fluctuations and nuclear structure, as well as input for possible in-depth dynamical studies for experimental measurement.