Scaling approach to nuclear structure in high-energy heavy-ion collisions

Abstract

In high-energy heavy-ion collisions, the initial condition of the produced quark-gluon plasma (QGP) and its evolution are sensitive to collective nuclear structure parameters describing the shape and radial profiles of the nuclei. We find a general scaling relation between these parameters and many experimental observables such as elliptic flow, triangular flow, and particle multiplicity distribution. In particular, the ratios of observables between two isobar systems depend only on the differences of these parameters, but not on the details of the final state interactions, hence offering a new way to constrain the QGP initial condition. Using this scaling relation, we show how the structure parameters of ${}_{44}^{96}$Ru and ${}_{40}^{96}$Zr conspire to produce the rich centrality dependences of these ratios, as measured by the STAR Collaboration. Our scaling approach demonstrates that isobar collisions are a precision tool to probe the initial condition of heavy-ion collisions, as well as the collective nuclear structures, including the neutron skin, of the atomic nuclei across energy scales.

arXiv comment5 pages and 3 figures, plus supplemental materials, published version

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Cited by in corpus (17)

• Probing triaxial deformation of atomic nuclei in high-energy heavy ion collisions
• Determination of the neutron skin of ${}^{208}$Pb from ultrarelativistic nuclear collisions
• Exploring QCD matter in extreme conditions with Machine Learning
• Imaging the initial condition of heavy-ion collisions and nuclear structure across the nuclide chart
• Separating the impact of nuclear skin and nuclear deformation in high-energy isobar collisions
• Inferring nuclear structure from heavy isobar collisions using Trajectum
• Impact of nuclear shape fluctuations in high-energy heavy ion collisions
• Precision tests of the nonlinear mode coupling of anisotropic flow via high-energy collisions of isobars
• Examination of nucleon distribution with Bayesian imaging for isobar collisions
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