Speaker
Description
The equation of state (EoS) at finite baryon chemical potential remains an elusive target for direct lattice QCD calculations, constituting a highly challenging topic. To address this issue, we have developed a novel Deep-learning quasi-parton model, which is constructed using three deep neural networks, to capture the fundamental properties of hot and dense QCD matter. Each neural network represents three distinct quasi-particle masses that are contingent upon the temperature (T) and baryon chemical potential ($\mu_B$). The resulting EoS derived from the quasi-particle model demonstrates strong agreement with lattice QCD results using Taylor expansion techniques. This provides a robust framework for approximating the EoS of QCD matter at finite baryon chemical potentials, thereby enhancing our theoretical understanding of QCD matter under extreme conditions.
| Category | Theory |
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