189 citations · 739 across the 31 of their papers we have counts for
7 papers · 2 filters
New constraints on equation of state of hot QCD matter
Lu-Meng Liu, Jinhui Chen, Xu-Guang Huang +3
The longitudinal structure of the quark-gluon plasma(QGP) remains a key challenge in heavy-ion physics. In this Letter, we propose a novel observable, event-by-event mean transvers…
Scaling approach to rigid and soft nuclear deformation through flow fluctuations in high-energy nuclear collisions
Lumeng Liu, Chunjian Zhang, Jinhui Chen +3
The nature of octupole deformation, whether static or vibrational, remains an open question in nuclear physics. Here, we propose a scaling approach to probe this ambiguity by trian…
Symmetric-asymmetric collision comparison: disentangling nuclear structure and subnucleonic structure effects for small system flow
Shengli Huang, Jiangyong Jia, Chunjian Zhang
Previous flow measurements in small collision systems were mostly based on highly asymmetric collisions (+Pb, +Au, +Au, He+Au), where both nuclear structure and subn…
Sources of Radial Flow Fluctuations in the Quark-Gluon Plasma
Jiangyong Jia
The differential radial flow fluctuation has emerged as a new probe of the quark-gluon plasma. However, its characteristic rise-and-fall pattern with $p_{\mat…
Imprints of octupole collectivity in uranium-238 on relativistic heavy-ion flow observables
Chunjian Zhang, Jiangyong Jia, Jinhui Chen +2
Some atomic nuclei exhibit enhanced octupole collectivity, reflected in finite reflection-asymmetric multipole correlations rather than necessarily in a rigid static pear-shaped gr…
Directly probing existence of -cluster structure in Ne by relativistic heavy-ion collisions
Lu-Meng Liu, Hai-Cheng Wang, Song-Jie Li +5
Can relativistic heavy-ion collisions only probe the global shape of colliding nuclei, or their detailed internal structure as well? Taking Ne as an example, we attempt to d…
Validation and extrapolation of atomic mass with physics-informed fully connected neural network
Yiming Huang, Jinhui Chen, Jiangyong Jia +3
Machine learning offers a powerful framework for validating and predicting atomic mass. We compare three improved neural network methods for representation and extrapolation for at…