论文标题
6d（2,0）的自举，带软性批评
6D (2,0) Bootstrap with soft-Actor-Critic
论文作者
论文摘要
我们使用软性批评（SAC）算法作为随机优化器进行数值研究6D（2,0）超符号引导程序。我们专注于能量摩托车多重组中标量超符号初选的四点函数。从超级限制开始，我们对绝热的中央电荷进行搜索，并为80个CFT数据的集合得出了两条曲线（其中70个数据对应于未受保护的长长多数，而10则对应于受保护的短多个）。我们猜想这两条曲线捕获了A-和D系列（2,0）理论。与来自标准数值引导方法的现有界限以及使用OPE反转公式获得的数据相比，我们的结果具有竞争力。在本文中，我们还将释放我们对SAC算法的Python实现。本文讨论了此软件包的主要功能功能。
We study numerically the 6D (2,0) superconformal bootstrap using the soft-Actor-Critic (SAC) algorithm as a stochastic optimizer. We focus on the four-point functions of scalar superconformal primaries in the energy-momentum multiplet. Starting from the supergravity limit, we perform searches for adiabatically varied central charges and derive two curves for a collection of 80 CFT data (70 of these data correspond to unprotected long multiplets and 10 to protected short multiplets). We conjecture that the two curves capture the A- and D-series (2,0) theories. Our results are competitive when compared to the existing bounds coming from standard numerical bootstrap methods, and data obtained using the OPE inversion formula. With this paper we are also releasing our Python implementation of the SAC algorithm, BootSTOP. The paper discusses the main functionality features of this package.
