论文标题
通过机器学习的核液态气相过渡
Nuclear liquid-gas phase transition with machine learning
论文作者
论文摘要
机器学习技术显示了它们在研究凝结物理学中研究相变的能力。在这里，我们采用机器学习技术来研究核液态气相变。我们采用了无监督的学习，并直接从重型离子反应的最终状态原始实验数据中对核的液相和气相进行了分类。基于结合受监督和无监督学习的混乱方案，我们获得了核液体 - 气体相变的限制温度。它的价值$ 9.24 \ pm0.04〜 \ rm mev $与传统热量曲线方法获得的价值一致。我们的研究探讨了将机器学习技术与重离子实验数据相结合的范式，并且它也具有研究其他不可控制系统（如QCD物质）的相变。
The machine-learning techniques have shown their capability for studying phase transitions in condensed matter physics. Here, we employ the machine-learning techniques to study the nuclear liquid-gas phase transition. We adopt an unsupervised learning and classify the liquid and gas phases of nuclei directly from the final state raw experimental data of heavy-ion reactions. Based on a confusion scheme which combines the supervised and unsupervised learning, we obtain the limiting temperature of the nuclear liquid-gas phase transition. Its value $9.24\pm0.04~\rm MeV$ is consistent with that obtained by the traditional caloric curve method. Our study explores the paradigm of combining the machine-learning techniques with heavy-ion experimental data, and it is also instructive for studying the phase transition of other uncontrollable systems, like QCD matter.
