第十届粒子所“青年之星”2024年度学术论坛

Machine learning has been widely applied in physics research. Although unsupervised learning can extract the critical points of phase transitions, the percolation model remains a challenge. Unsupervised learning using the raw configurations of the percolation model fails to capture the critical points. To capture the configuration characteristics of the percolation model, this paper proposes...

In recent years, neural networks have increasingly been employed to identify critical points of phase transitions. For the tricritical directed percolation model, its steady-state configurations encompass both first-order and second-order phase transitions. Due to the presence of crossover effects, identifying the critical points of phase transitions becomes challenging. This study utilizes...

In this study, we employ a superstatistical approach to construct $q$ exponential and $q$ Maxwell Boltzmann complex networks, generalizing the concept of scale-free networks. By adjusting the crossover parameter $\lambda$, we control the degree of the $q$ exponential plateau at low node degrees, allowing a smooth transition to pure power-law degree distributions. Similarly, the parameter $b$...

The QCD critical point is suggested to be in the same universality class as the 3D Ising model, which implies that the behavior of thermodynamic observables near the QCD critical point can be described by the critical exponents and scaling laws of the Ising model. The percolation study offers valuable insights into the characteristics of phase transition, revealing the underlying mechanisms...