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arXiv 2203.13876nucl-thhep-ph
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利用主动学习绘制含临界点的QCD状态方程的热力学稳定性图谱

Mapping out the thermodynamic stability of a QCD equation of state with a critical point using active learning

D. Mroczek, M. Hjorth-Jensen, J. Noronha-Hostler, P. Parotto, C. Ratti, R. Vilalta

AI总结:

本文利用主动学习框架高效识别含临界点的QCD状态方程中由自由参数组合导致的非因果或不稳定实现,仅用少量参数范围训练即可在全参数空间达到96%以上准确率,并可推广至其他含参数空间-类别对应关系的状态方程。

摘要:

束流能量扫描理论(BEST)合作组的状态方程(EoS)将三维伊辛模型临界点纳入来自格点模拟的量子色动力学(QCD)状态方程。然而,该状态方程包含4个与QCD相图中临界区域大小和位置相关的自由参数。自由参数的某些组合会导致非因果或不稳定的状态方程实现,这些实现不应被考虑。在这项工作中,我们使用主动学习框架高效地排除病态状态方程。我们发现,仅检查参数范围的一小部分就足以构建在整个参数空间上准确率超过96%的算法。尽管本工作聚焦于一个具体案例,但我们的方法可以推广到任何包含参数空间-类别对应关系的状态方程。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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