论文标题
一般相对论中的深度学习度量探测器
Deep Learning Metric Detectors in General Relativity
论文作者
论文摘要
我们考虑使用弯曲空间中的测试粒子大地测量学对度量检测器的深度学习概念问题（DL）。从一般坐标转换的角度强调了DL度量探测器的优势。如果在任何时候无法通过任何DL度量检测器区分它们的地理图像数据，则将两个给定的指标（两个空间）定义为通过DL等轴测图连接。广泛探索了DL等轴测图何时出现的基本问题。如果通过DL等距连接的两个空间是量子重力理论的叠加，那么即使在DL度量探测器观察到超构态之后，测量后状态也可能仍处于相同的叠加。我们还展示了2+1维抗DE保姆（ADS）空间中的度量检测DL，以估计宇宙学常数和棕色 - 赫尼的电荷。在ADS/CFT对应词典中，可以预期，ADS批量区域中的这类度量检测器对应于ADS边界的CFT中的量子测量设备。
We consider conceptual issues of deep learning (DL) for metric detectors using test particle geodesics in curved spacetimes. Advantages of DL metric detectors are emphasized from a view point of general coordinate transformations. Two given metrics (two spacetimes) are defined to be conneted by a DL isometry if their geodesic image data cannot be discriminated by any DL metric detector at any time. The fundamental question of when the DL isometry appears is extensively explored. If the two spacetimes connected by the DL isometry are in superposition of quantum gravity theory, the post-measurement state may be still in the same superposition even after DL metric detectors observe the superposed state. We also demonstrate metric-detection DL's in 2+1 dimensional anti-de Sitter (AdS) spacetimes to estimate the cosmological constants and Brown-Henneaux charges. In the AdS/CFT correspondence dictionary, it may be expected that such metric detectors in the AdS bulk region correspond to quantum measurement devices in the CFT at the AdS boundary.
