839 citations · 18.9k across the 244 of their papers we have counts for
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Machine Learning in Nuclear Physics
Amber Boehnlein, Markus Diefenthaler, Cristiano Fanelli +15
Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics…
Modification of leptonic invariant mass in ultrarelativistic heavy ion collisions as a measure of the electromagnetic field
Yifeng Sun, Vincenzo Greco, Xin-Nian Wang
An extraordinary strong magnetic field, Gauss, is expected to be generated in non-central ultrarelativistic heavy ion collisions and it is envisaged to induc…
QLBT: A linear Boltzmann transport model for heavy quarks in a quark-gluon plasma of quasi-particles
Feng-Lei Liu, Wen-Jing Xing, Xiang-Yu Wu +3
We develop a new heavy quark transport model, QLBT, to simulate the dynamical propagation of heavy quarks inside the quark-gluon plasma (QGP) created in relativistic heavy-ion coll…
Probing criticality with deep learning in relativistic heavy-ion collisions
Yige Huang, Long-Gang Pang, Xiaofeng Luo +1
Systems with different interactions could develop the same critical behaviour due to the underlying symmetry and universality. Using this principle of universality, we can embed cr…
(3+1)-D viscous hydrodynamics CLVisc at finite net baryon density: identified particle spectra, anisotropic flows and flow fluctuations across BES energies
Xiang-Yu Wu, Guang-You Qin, Long-Gang Pang +1
To study the bulk properties of the quark-gluon-plasma (QGP) produced at the beam energy scan (BES) energies at the Relativistic Heavy Ion Collider (RHIC), we extend the (3+1)-dime…
Parton rescattering and gluon saturation in dijet production at EIC
Yuan-Yuan Zhang, Xin-Nian Wang
Large angle gluon radiations induced by multiple parton scatterings contribute to dijet production in deeply inelastic scattering off a large nucleus at the Electron-Ion Collider.…