ScalingStacks

Proof. [00DK]

Original official author HTML, exact retained edition. Historical TeX conversion verdicts remain unchanged. Cited-edition alignment and mathematical self-containment are not assessed.

Complete original source context · Original author HTML

Proof.

Given any point x∈EJx\in E_{J} we can find k≫1k\gg 1 and sections s0,…,sN∈H0​(𝔛,𝔏k)s_{0},\dots,s_{N}\in H^{0}(\mathfrak{X},\mathfrak{L}^{k}) so that in some adapted coordinate chart VxV_{x} near xx we have that none of the sections s0,sm+1,…,sNs_{0},s_{m+1},\dots,s_{N} vanishes, while sj=0s_{j}=0 is a defining equation for EjE_{j}, 1⩽j⩽m1\leqslant j\leqslant m, and so sj/s0s_{j}/s_{0} is comparable to zjz_{j} for 1⩽j⩽m1\leqslant j\leqslant m.

We construct a Kähler metric ωx,t′\omega_{x,t}^{\prime} on XtX_{t} by pulling back a suitable toric metric on ℙN\mathbb{P}^{N} , which on the complement of the zeros of all the sis_{i}’s is given by

ωx,t′=1k​i​∂∂¯​u​(log⁡|s1/s0|log⁡|t|,…,log⁡|sN/s0|log⁡|t|),\omega^{\prime}_{x,t}=\frac{1}{k}i\partial\bar{\partial}u\left(\frac{\log|s_{1}/s_{0}|}{\log|t|},\dots,\frac{\log|s_{N}/s_{0}|}{\log|t|}\right),

where u⁡(x1,…,xN)u(x_{1},\dots,x_{N}) is a smooth convex function in ℝN\mathbb{R}^{N} which is asymptotic to v⁡(x1,…,xN)=max⁡(0,x1,…,xN)v(x_{1},\dots,x_{N})=\max(0,x_{1},\dots,x_{N}) at infinity, and with D2​u⩾C−1​IdD^{2}u\geqslant C^{-1}\mathrm{Id} on a ball of radius comparable to 1 containing the image of Vx∩XtV_{x}\cap X_{t} in the logarithmic coordinates. For example, an explicit such uu can be produced as the convolution of vv with a smooth mollifier. By construction, ωx,t′\omega^{\prime}_{x,t} lies in the class 1|log⁡|t||​c1​(𝔏)|Xt\frac{1}{|\log|t||}c_{1}(\mathfrak{L})|_{X_{t}}, and it satisfies (3.2) on Vx∩XtV_{x}\cap X_{t}.

We then choose finitely many x(1),…,x(M)∈EJx^{(1)},\dots,x^{(M)}\in E_{J} such that the corresponding Vx(1),…,Vx(M)V_{x^{(1)}},\dots,V_{x^{(M)}} cover EJE_{J}, let UU be their union, and define

ωt′=1M​∑k=1Mωx(k),t′.\omega^{\prime}_{t}=\frac{1}{M}\sum_{k=1}^{M}\omega^{\prime}_{x^{(k)},t}.

This is our desired Kähler metric on XtX_{t} in 1|log⁡|t||​c1​(𝔏)|Xt\frac{1}{|\log|t||}c_{1}(\mathfrak{L})|_{X_{t}} which satisfies (3.2) in adapted coordinate charts on U∩XtU\cap X_{t}.

Next, we consider the function

ρ^=A​∑j=1m(x~j2−bj)2−1=A​∑j=1m(log⁡rj2​log⁡|t|−bj)2−1,\hat{\rho}=A\sum_{j=1}^{m}\left(\frac{\tilde{x}_{j}}{2}-b_{j}\right)^{2}-1=A\sum_{j=1}^{m}\left(\frac{\log r_{j}}{2\log|t|}-b_{j}\right)^{2}-1,

on U\⋃i∈IEiU\backslash\bigcup_{i\in I}E_{i}. Choosing the constants bjb_{j} in the strict interior of ΔJ\Delta_{J} we can ensure the minimum of ρ^\hat{\rho} on U∩XtU\cap X_{t} equals −1-1, and choosing AA suitably large independent of tt, we can ensure {ρ^⩽0}\{\hat{\rho}\leqslant 0\} is compactly contained in UU. Now we define

ρt={min⁡(ρ^,0)|Xt on ​U∩Xt0 on ​Xt\U,\rho_{t}=\begin{cases}\min(\hat{\rho},0)|_{X_{t}}\quad&\text{ on }U\cap X_{t}\\ 0\quad&\text{ on }X_{t}\backslash U\end{cases},

which satisfies the requirements in (a). We let Bt={ρt<0}⊂XtB_{t}=\{\rho_{t}<0\}\subset X_{t}. Lastly, (3.3) follows immediately from part (a) and (3.2). ∎

Original mathematics by the credited authors. Source-backed reader collection; mathematical self-containment is not assessed.