论文标题
关于交错夸克的狄拉克特征值光谱的深度学习研究
Deep learning study on the Dirac eigenvalue spectrum of staggered quarks
论文作者
论文摘要
我们使用深度学习（DL）技术研究了在Dirac特征值光谱上交错的夸克的手性。 Kluberg-stern构建交错双线操作员的方法保守了连续性的属性，例如递归关系，手性的独特性和病房的身份，该特性在Chirality操作员的矩阵元素中导致了独特而典型的模式（我们称其为“泄漏模式（LP）”）。 DL分析给出了$ 99.4（2）\％$的普通量规配置的准确性和$ 0.998 $ AUC（ROC曲线下的区域），用于分类DIRAC特征值频谱中的非零模式八位位。它确认泄漏模式在正常仪表配置上是通用的。事实证明，多层感知器（MLP）方法是我们在LP上研究的最佳DL模型。
We study the chirality of staggered quarks on the Dirac eigenvalue spectrum using deep learning (DL) techniques. The Kluberg-Stern method to construct staggered bilinear operators conserves continuum property such as recursion relations, uniqueness of chirality, and Ward identities, which leads to a unique and characteristic pattern (we call it "leakage pattern (LP)") in the matrix elements of the chirality operator sandwiched between two quark eigenstates of staggered Dirac operator. DL analysis gives $99.4(2)\%$ accuracy on normal gauge configurations and $0.998$ AUC (Area Under ROC Curve) for classifying non-zero mode octets in the Dirac eigenvalue spectrum. It confirms that the leakage pattern is universal on normal gauge configurations. The multi-layer perceptron (MLP) method turns out to be the best DL model for our study on the LP.
