6D（2，0）Bootstrap with soft-Actor-Critic
6D (2,0) Bootstrap with soft-Actor-Critic
摘要：
我们数值研究了6D（2，0）超共形引导使用软演员-批判（SAC）算法作为随机优化。我们着重研究了能量-动量多重态中标量超共形原色的四点函数。从超重力极限开始，我们进行搜索的几何变化的中心荷和推导出两条曲线的收集80 CFT数据（70这些数据对应于未受保护的长多重峰和10保护的短多重峰）。我们猜想这两条曲线都包含了A-和D-级数（2，0）理论。我们的结果是竞争力相比，现有的界限来自标准的数值引导方法，并使用OPE反演公式获得的数据。在本文中，我们还发布了SAC算法的Python实现BootSTOP。本文讨论了该软件包的主要功能特性。
英文摘要：
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.
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