论文标题
机器学习$ {\ cal n} = 8，d = 5 $测量超级
Machine Learning ${\cal N}=8, D=5$ Gauged Supergravity
论文作者
论文摘要
5个球的类型IIB字符串理论产生$ {\ cal n} = 8，因此（6）$ $衡量的超级重力在五个维度上。这是由于这是广告/CFT对应的最广泛研究示例的背景，我们对其关键点进行了调查。标量歧管是$ e_ {6（6）}/usp（8）$ cOSET，挑战是它是42维。我们使用TensorFlow采用机器学习方法来解决该问题，这导致已知关键点的数量大幅增加。我们的32个关键点列表包含所有五个先前已知的点，包括$ {\ cal n} = 2 $ supersymmetric Point由Khavaev，Pilch和Warner确定。
Type IIB string theory on a 5-sphere gives rise to ${\cal N}=8, SO(6)$ gauged supergravity in five dimensions. Motivated by the fact that this is the context of the most widely studied example of the AdS/CFT correspondence, we undertake an investigation of its critical points. The scalar manifold is an $E_{6(6)}/USp(8)$ coset, and the challenge is that it is 42-dimensional. We take a Machine Learning approach to the problem using TensorFlow, and this results in a substantial increase in the number of known critical points. Our list of 32 critical points contains all five of the previously known ones, including an ${\cal N}=2$ supersymmetric point identified by Khavaev, Pilch and Warner.
