论文标题
使用主动学习绘制QCD状态方程的热力学稳定性
Mapping out the thermodynamic stability of a QCD equation of state with a critical point using active learning
论文作者
论文摘要
光束能量扫描理论（最佳）协作状态方程（EOS）将3D ISING模型临界点从晶格模拟中的状态量子染色体动力学（QCD）方程中纳入。但是，它包含与QCD相图中关键区域的大小和位置相关的4个自由参数。自由参数的某些组合会导致不应考虑的EOS的可支柱或不稳定的EOS实现。在这项工作中，我们使用一个主动的学习框架有效排除病理EOS。我们发现，检查参数范围的一小部分的稳定性和因果关系足以构建在整个参数空间中以$ 96％精度执行的算法。尽管在这项工作中，我们专注于特定情况，但我们的方法可以推广到包含参数太空级对应的任何EOS。
The Beam Energy Scan Theory (BEST) collaboration's equation of state (EoS) incorporates a 3D Ising model critical point into the Quantum Chromodynamics (QCD) equation of state from lattice simulations. However, it contains 4 free parameters related to the size and location of the critical region in the QCD phase diagram. Certain combinations of the free parameters lead to acausal or unstable realizations of the EoS that should not be considered. In this work, we use an active learning framework to rule out pathological EoS efficiently. We find that checking stability and causality for a small portion of the parameters' range is sufficient to construct algorithms that perform with $>$96% accuracy across the entire parameter space. Though in this work we focus on a specific case, our approach can be generalized to any EoS containing a parameter space-class correspondence.
