185 citations · 1.3k across the 49 of their papers we have counts for
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Phase Transition Study meets Machine Learning
Yu-Gang Ma, Long-Gang Pang, Rui Wang +1
In recent years, machine learning (ML) techniques have emerged as powerful tools for studying many-body complex systems, and encompassing phase transitions in various domains of ph…
Mass and tidal parameter extraction from gravitational waves of binary neutron stars mergers using deep learning
Shriya Soma, Horst Stöcker, Kai Zhou
Gravitational Waves (GWs) from coalescing binaries carry crucial information about their component sources, like mass, spin and tidal effects. This implies that the analysis of GW…
High energy nuclear physics meets Machine Learning
Wan-Bing He, Yu-Gang Ma, Long-Gang Pang +2
Though being seemingly disparate and with relatively new intersection, high energy nuclear physics and machine learning have already begun to merge and yield interesting results du…
Model dependence of the number of participant nucleons and observable consequences in heavy-ion collisions
Manjunath Omana Kuttan, Jan Steinheimer, Kai Zhou +2
The centrality determination and the estimated fluctuations of number of participant nucleons in Au-Au collisions at 1.23 GeV beam kinetic energy suffers from severe…
Bottom energy loss and non-prompt production in relativistic heavy ion collisions
Meimei Yang, Shiqi Zheng, Bo Tong +4
We study the momentum and centrality dependence of the non-prompt nuclear modification factors (), which comes from the hadrons decay, in Pb-Pb collisions at the…
Examination of nucleon distribution with Bayesian imaging for isobar collisions
Yi-Lin Cheng, Shuzhe Shi, Yu-Gang Ma +2
Relativistic collision of isobaric systems is found to be valuable in differentiating the nucleon distributions for nuclei with the same mass number. In recent contrast experiment…