论文标题
将小黑洞粘在初始数据集中
Gluing small black holes into initial data sets
论文作者
论文摘要
我们证明了在$ n \ geq 3 $空间维度中的一般相对论约束方程(有或没有宇宙学常数)的强烈局部胶合结果。我们将$ε$ - 对一个$ \hatγ,\ hat k)$的$ε$ - 在x $附近的附近粘贴到x $ in x $的附近,在另一个初始数据集$(x,γ,k)$的另一个初始数据集$(x,γ,k)$附近,在局部通用条件下(在$ \ mathak $ \ mathak $ \ mathak $ c}附近。由于缩放参数$ε$趋向于$ 0 $,因此recrecalings $ \ frac {x}ε$在$ \ mathfrak {p} $周围$ x $上的$ x $的$ x $变为渐近平面数据集的渐近平坦坐标;另一方面,在$ \ mathfrak {p} $的任何社区之外,胶合的初始数据会收集回到$(γ,k)$。我们构建的初始数据以$ε$和(重新定化的)空间坐标共同享有多均匀的规律性。 将我们的构造应用于单位质量黑洞数据集$(x,γ,k)$和适当增强的KERR初始数据集$(\hatγ,\ hat k)$产生的初始数据,这些数据可以猜想地演变为单位质量的极端质量比率,质量$ $ $ $ $ Black Hole。 该证明结合了Corvino和Schoen引入的胶合方法的一种变体,以及源自梅尔罗斯作品中的几何分析技术。在技术层面上,我们为线性约束图介绍了可溶解性理论的完全几何微局部处理。
We prove a strong localized gluing result for the general relativistic constraint equations (with or without cosmological constant) in $n\geq 3$ spatial dimensions. We glue an $ε$-rescaling of an asymptotically flat data set $(\hatγ,\hat k)$ into the neighborhood of a point $\mathfrak{p}\in X$ inside of another initial data set $(X,γ,k)$, under a local genericity condition (non-existence of KIDs) near $\mathfrak{p}$. As the scaling parameter $ε$ tends to $0$, the rescalings $\frac{x}ε$ of normal coordinates $x$ on $X$ around $\mathfrak{p}$ become asymptotically flat coordinates on the asymptotically flat data set; outside of any neighborhood of $\mathfrak{p}$ on the other hand, the glued initial data converge back to $(γ,k)$. The initial data we construct enjoy polyhomogeneous regularity jointly in $ε$ and the (rescaled) spatial coordinates. Applying our construction to unit mass black hole data sets $(X,γ,k)$ and appropriate boosted Kerr initial data sets $(\hatγ,\hat k)$ produces initial data which conjecturally evolve into the extreme mass ratio inspiral of a unit mass and a mass $ε$ black hole. The proof combines a variant of the gluing method introduced by Corvino and Schoen with geometric singular analysis techniques originating in Melrose's work. On a technical level, we present a fully geometric microlocal treatment of the solvability theory for the linearized constraints map.