This paper was converted on www.awesomepapers.org from LaTeX by an anonymous user.
Want to know more? Visit the Converter page.

On the probability of fast exits and long stays of planar Brownian motion in simply connected domains

Dimitrios Betsakos 1, Maher Boudabra 2, and Greg Markowsky 2
[email protected]    [email protected]    [email protected]
1
Department of Mathematics, Aristotle University of Thessaloniki, Greece
2Department of Mathematics, Monash University, Australia
Abstract

Let TDT^{D} denote the first exit time of a planar Brownian motion from a domain DD. Given two simply connected planar domains U,WU,W\neq{\mathbb{C}} containing 0, we investigate the cases in which we are more likely to have fast exits (meaning for instance 𝐏(TU<t)>𝐏(TW<t){\bf P}(T^{U}<t)>{\bf P}(T^{W}<t) for tt small) from UU than from WW, or long stays (meaning 𝐏(TU>t)>𝐏(TW>t){\bf P}(T^{U}>t)>{\bf P}(T^{W}>t) for tt large). We prove several results on these questions. In particular, we show that the primary factor in the probability of fast exits is the proximity of the boundary to the origin, while for long stays an important factor is the moments of the exit time. The complex analytic theory that motivated our inquiry is also discussed.

1 Introduction

The distribution of the exit time of planar Brownian motion from a domain measures in some sense the size of the domain. It can also be used for the study of analytic functions via the conformal invariance of Brownian motion.

Let Zt=Xt+iYtZ_{t}=X_{t}+iY_{t}, t0t\geq 0 denote standard Brownian motion moving in the plane and starting from the origin, (that is, Z0=0Z_{0}=0 almost surely). We denote by 𝐏{\bf P} and 𝐄{\bf E} the corresponding probability measure and expectation, respectively. For a domain DD containing 0 in the complex plane {\mathbb{C}}, we denote by TDT^{D} the first exit time of ZtZ_{t} from DD; that is

TD=inf{t>0:ZtD}.T^{D}=\inf\{t>0:Z_{t}\notin D\}.

Suppose also that ff is a univalent function in the well-known class 𝒮\mathcal{S}. Thus ff is a function univalent and holomorphic in the unit disk 𝔻{\mathbb{D}} with f(0)=0f(0)=0 and f(0)=1f^{\prime}(0)=1. By a classical result of P. Lévy, the image of ZtZ_{t} under ff is a Brownian motion with a time change. We describe now a precise version of this conformal invariance of Brownian motion; (see [Dav79, §2]). Let

ρf(s)=0s|f(Zt)|2𝑑t,     0s<T𝔻.\rho_{f}(s)=\int_{0}^{s}|f^{\prime}(Z_{t})|^{2}\,dt,\;\;\;\;\;0\leq s<T^{\mathbb{D}}.

Observe that ρf\rho_{f} is almost surely strictly increasing and set

Wt=f(Zρf1(t)),   0t<ρf(T𝔻).W_{t}=f(Z_{\rho_{f}^{-1}(t)}),\;\;\;0\leq t<\rho_{f}(T^{\mathbb{D}}).

Define also Wρf(T𝔻)=limtT𝔻Wρf(t)W_{\rho_{f}(T^{\mathbb{D}})}=\lim_{t\to T^{\mathbb{D}}}W_{\rho_{f}(t)} and

Wρf(T𝔻)+t=Wρf(T𝔻)+(ZT𝔻+tZT𝔻),t>0.W_{\rho_{f}(T^{\mathbb{D}})+t}=W_{\rho_{f}(T^{\mathbb{D}})}+(Z_{T^{\mathbb{D}}+t}-Z_{T^{\mathbb{D}}}),\;\;\;\;t>0.

Note that ff being univalent and holomorphic implies that P(ρf(T𝔻)<)=1P(\rho_{f}(T^{\mathbb{D}})<\infty)=1; this is a nontrivial statement, but is shown for instance in [Bur77, p.198]. Lévy’s theorem now asserts that {Wt,t0}\{W_{t},t\geq 0\} is standard planar Brownian motion starting from the origin.

We set

ν(f)=ρf(T𝔻)=0T𝔻|f(Zt)|2𝑑t\nu(f)=\rho_{f}(T^{\mathbb{D}})=\int_{0}^{T^{\mathbb{D}}}|f^{\prime}(Z_{t})|^{2}\,dt

and observe that ν(f)\nu(f) is the first exit time of WtW_{t} from f(𝔻)f({\mathbb{D}}); that is, ν(f)=Tf(𝔻)\nu(f)=T^{f({\mathbb{D}})}. “The distribution of ν(f)\nu(f) is an intuitively appealing measure of the size of f(𝔻)f({\mathbb{D}})[Dav79]. In several classical extremal problems for functions in the class 𝒮{\mathcal{S}}, the identity function I(z)=zI(z)=z is the “smallest” function while the Koebe function k(z)=z/(1z)2k(z)=z/(1-z)^{2} is the “largest” function in 𝒮\mathcal{S}. B. Davis [Dav79] conjectured that

(1.1) 𝐄[Φ(ν(I))]𝐄[Φ(ν(f))],{\bf E}[\Phi(\nu(I))]\leq{\bf E}[\Phi(\nu(f))],

for all f𝒮f\in\mathcal{S} and all increasing convex functions Φ:[0,)\Phi:[0,\infty)\to{\mathbb{R}}, and suggested that perhaps

𝐏(ν(I)>t)𝐏(ν(g)>t),{\bf P}(\nu(I)>t)\leq{\bf P}(\nu(g)>t),

for all t>0t>0 and g𝒮g\in\mathcal{S}. Apropos the first conjecture T. McConnell [McC85] proved that

(1.2) 𝐄[ν(I)p]𝐄[ν(f)p],   0<p<,{\bf E}[\nu(I)^{p}]\leq{\bf E}[\nu(f)^{p}],\;\;\;0<p<\infty,

but the full conjecture remains open, as far as we know. McConnell also disproved the second conjecture by finding functions g𝒮g\in\mathcal{S} such that

(1.3) 𝐏(ν(I)>t)>𝐏(ν(g)>t),{\bf P}(\nu(I)>t)>{\bf P}(\nu(g)>t),

for all sufficiently small t>0t>0. Davis also asked in what sense, with regard to ν(f)\nu(f), the Koebe function is the largest in 𝒮\mathcal{S}.

Motivated by these developments, we have considered the following questions. Given two simply connected planar domains U,WU,W\neq{\mathbb{C}} containing 0, what sufficient conditions can we place on the domains so that we are more likely to have fast exits (meaning for instance 𝐏(TU<t)>𝐏(TW<t){\bf P}(T^{U}<t)>{\bf P}(T^{W}<t) for tt small) from UU than from WW, or long stays (meaning 𝐏(TU>t)>𝐏(TW>t){\bf P}(T^{U}>t)>{\bf P}(T^{W}>t) for tt large). We have found that the primary factor influencing the probability of fast exits is the proximity of the boundary to the origin. In order to make this more precise, let us introduce the following notation. For any simply connected domain VV, let

d(V)=inf{|z|:zV}.d(V)=\inf\{|z|:z\in\partial V\}.

We then have the following theorem, which is the main result of the paper.

Theorem 1

Suppose that d(U)<12d(W)d(U)<\frac{1}{\sqrt{2}}d(W). Then, for all sufficiently small t>0t>0,

(1.4) 𝐏(TU<t)>𝐏(TW<t).{\bf P}(T^{U}<t)>{\bf P}(T^{W}<t).

In fact,

limt0+𝐏(TU<t)𝐏(TW<t)=.\lim_{t\longrightarrow 0^{+}}\frac{{\bf P}(T^{U}<t)}{{\bf P}(T^{W}<t)}=\infty.

We do not know whether 12\frac{1}{\sqrt{2}} is the optimal constant; it may even be that it may be replaced by 1. If so, this would be a surprising property of planar Brownian motion. Evidence for this possibility can be found in the fact that it is true if UU is a half-plane. This is discussed in more detail in Section 3.

Naturally, it would be nice to have an analog for long stays. We believe that the important factor for long stays in domains is the moments of the exit time. To be precise, for domain VV let

H(V)=sup{p>0:𝐄[(TV)p]<};{\rm H}(V)=\sup\{p>0:{\bf E}[(T^{V})^{p}]<\infty\};

note that H(V){\rm H}(V) is proved in [Bur77] to be exactly equal to half of the Hardy number of VV, a purely analytic quantity, as defined in [Han70], and is therefore calculable for a number of common domains. Furthermore H(V)14{\rm H}(V)\geq\frac{1}{4} as long as VV\neq{\mathbb{C}}. We have the following simple result.

Proposition 1

Suppose that H(U)>H(W){\rm H}(U)>{\rm H}(W). Then

(1.5) lim supt𝐏(TW>t)𝐏(TU>t)=.\limsup_{t\longrightarrow\infty}\frac{{\bf P}(T^{W}>t)}{{\bf P}(T^{U}>t)}=\infty.

We conjecture that this proposition is true with the lim sup\limsup replaced by lim\lim, but have not been able to prove it (except when WW is either a half-plane or quarter-plane). This is discussed in detail in the final section.

2 Proofs

In this section we prove Theorem 1 and Proposition 1.

2.1 Proof of Theorem 1

The proof of Theorem 1 is based on the strong Markov property and the explicit formula for the transition density of one-dimensional Brownian motion. Some of the estimates used are probably known to experts; we hope, however, that their elementary derivation and their use in the study of univalent functions are of some interest.

In what follows, XtX_{t} will denote one-dimensional Brownian motion. The corresponding probability measure with starting point xx\in{\mathbb{R}} will be denoted by 𝐏x{\bf P}^{x}. The first hitting time of a point yy\in{\mathbb{R}} will be denoted by τy\tau_{y}. The following well known equality comes easily from the reflection principle; see

[Dur84, p.23]:

(2.1) 𝐏0(τat)=2𝐏0(Xta),a>0.{\bf P}^{0}(\tau_{a}\leq t)=2{\bf P}^{0}(X_{t}\geq a),\;\;\;\;a>0.

We will use the standard notation for the transition density function of Brownian motion:

ps(x)=12πsex2/2s,x,s>0.p_{s}(x)=\frac{1}{\sqrt{2\pi s}}\;\;e^{-x^{2}/2s},\;\;\;x\in{\mathbb{R}},\;s>0.

It follows from elementary calculus that, for any δ>0\delta>0 there exists a constant C1>1C_{1}>1 such that for every y>δy>\delta,

(2.2) C11ey2yyex2𝑑xC1ey2y.C_{1}^{-1}\;\frac{e^{-y^{2}}}{y}\leq\int_{y}^{\infty}e^{-x^{2}}\,dx\leq C_{1}\;\frac{e^{-y^{2}}}{y}.

We begin by proving a preliminary proposition, then show how it extends to prove Theorem 1.

Proposition 2

Let Kα:=\(,α]K_{\alpha}:={\mathbb{C}}\backslash(-\infty,-\alpha] for any α\alpha with 0<α<120<\alpha<\frac{1}{\sqrt{2}}. Then

limt0+𝐏(TKα<t)𝐏(T𝔻<t)=.\lim_{t\longrightarrow 0^{+}}\frac{{\bf P}(T^{K_{\alpha}}<t)}{{\bf P}(T^{\mathbb{D}}<t)}=\infty.

This will be proved through a sequence of lemmas. In what follows, CC will denote a generic absolute constant that may change from line to line.

Lemma 1

For any ε>0\varepsilon>0, there exist constants δ,C2>0\delta,C_{2}>0 such that for every t(0,δ)t\in(0,\delta),

(2.3) 𝐏0(Xt/2>0,Xt<0)=0pt/2(y)ypt/2(x)𝑑x𝑑yC2teε2t.{\bf P}^{0}(X_{t/2}>0,X_{t}<0)=\int_{0}^{\infty}p_{t/2}(y)\int_{y}^{\infty}p_{t/2}(x)\,dx\,dy\geq C_{2}\sqrt{t}\;e^{-\frac{\varepsilon^{2}}{t}}.

Proof: By a change of variable and (2.2),

0pt/2(y)ypt/2(x)𝑑x𝑑y\displaystyle\!\!\!\!\!\!\!\!\!\!\!\!\int_{0}^{\infty}p_{t/2}(y)\int_{y}^{\infty}p_{t/2}(x)\,dx\,dy
\displaystyle\geq 0εpt/2(y)εpt/2(x)𝑑x𝑑y\displaystyle\int_{0}^{\varepsilon}p_{t/2}(y)\int_{\varepsilon}^{\infty}p_{t/2}(x)\,dx\,dy
=\displaystyle= (0εpt/2(y)𝑑y)(εpt/2(x)𝑑x)\displaystyle\left(\int_{0}^{\varepsilon}p_{t/2}(y)\,dy\right)\left(\int_{\varepsilon}^{\infty}p_{t/2}(x)\,dx\right)
=\displaystyle= (1πt0εey2/t𝑑y)(1πtεex2/t𝑑x)\displaystyle\left(\frac{1}{\sqrt{\pi t}}\int_{0}^{\varepsilon}e^{-y^{2}/t}\;dy\right)\,\left(\frac{1}{\sqrt{\pi t}}\int_{\varepsilon}^{\infty}e^{-x^{2}/t}\,dx\right)
=\displaystyle= C(0εteξ2𝑑ξ)(εteξ2𝑑ξ)\displaystyle C\,\left(\int_{0}^{\frac{\varepsilon}{\sqrt{t}}}e^{-\xi^{2}}\,d\xi\right)\;\left(\int_{\frac{\varepsilon}{\sqrt{t}}}^{\infty}e^{-\xi^{2}}\,d\xi\right)
\displaystyle\geq C2teε2t.\displaystyle C_{2}\sqrt{t}\;e^{-\frac{\varepsilon^{2}}{t}}.

     

Lemma 2

For any ε>0\varepsilon>0, there exists a constant C3>0C_{3}>0 such that for every t(0,1)t\in(0,1) and every y(α+ε)y\leq-(\alpha+\varepsilon),

(2.5) 𝐏y(Xsα,s[0,t/2])C3.{\bf P}^{y}\left(X_{s}\leq-\alpha,\;\;\forall s\in[0,t/2]\right)\geq C_{3}.

Proof: By (2.1) we have

𝐏y(Xsα,s[0,t/2])\displaystyle\!\!\!\!\!\!\!\!\!\!\!\!{\bf P}^{y}\left(X_{s}\leq-\alpha,\;\;\forall s\in[0,t/2]\right)
\displaystyle\geq 𝐏0(Xsε,s[0,1])\displaystyle{\bf P}^{0}(X_{s}\leq\varepsilon,\;\forall s\in[0,1])
=\displaystyle= 12𝐏0(X1ε)=C3>0\displaystyle 1-2{\bf P}^{0}(X_{1}\geq\varepsilon)=C_{3}>0

     

Lemma 3

For any ε>0\varepsilon>0, there exist constants δ,C4>0\delta,C_{4}>0 such that for every t(0,δ)t\in(0,\delta),

(2.7) 𝐏0(Xs=0for somes[t/2,t])C4teε2t.{\bf P}^{0}\left(X_{s}=0\;\;\;\hbox{for some}\;\;\;s\in[t/2,t]\right)\geq C_{4}\;\sqrt{t}\;e^{-\frac{\varepsilon^{2}}{t}}.

Proof: By the Markov property, the symmetry of Brownian motion, (2.1), and Lemma 1,

𝐏0(Xs=0for somes[t/2,t])\displaystyle\!\!\!\!\!\!\!\!\!\!\!\!{\bf P}^{0}(X_{s}=0\;\;\;\hbox{for some}\;\;\;s\in[t/2,t])
=\displaystyle= 20pt/2(y)𝐏y(Xs=0for somes[0,t/2])𝑑y\displaystyle 2\int_{0}^{\infty}p_{t/2}(y)\,{\bf P}^{y}(X_{s}=0\;\;\;\hbox{for some}\;\;\;s\in[0,t/2])\,dy
=\displaystyle= 20pt/2(y)𝐏y(τ0t/2)𝑑y\displaystyle 2\int_{0}^{\infty}p_{t/2}(y)\,{\bf P}^{y}(\tau_{0}\leq t/2)\,dy
=\displaystyle= 20pt/2(y)𝐏0(τyt/2)𝑑y\displaystyle 2\int_{0}^{\infty}p_{t/2}(y)\,{\bf P}^{0}(\tau_{y}\leq t/2)\,dy
=\displaystyle= 40pt/2(y)𝐏0(Xt/2y)𝑑y\displaystyle 4\int_{0}^{\infty}p_{t/2}(y)\,{\bf P}^{0}(X_{t/2}\geq y)\,dy
=\displaystyle= 40pt/2(y)ypt/2(x)𝑑x𝑑y\displaystyle 4\int_{0}^{\infty}p_{t/2}(y)\,\int_{y}^{\infty}p_{t/2}(x)\,dxdy
\displaystyle\geq C4teε2t.\displaystyle C_{4}\sqrt{t}\;e^{-\frac{\varepsilon^{2}}{t}}.

     

Lemma 4

For any ε>0\varepsilon>0, there exist constants δ,C5>0\delta,C_{5}>0 such that for every t(0,δ)t\in(0,\delta),

(2.9) 𝐏0(Xsα,s[t/2,t])C5te(α+ε)2t.{\bf P}^{0}\left(X_{s}\leq-\alpha,\;\;\forall s\in[t/2,t]\right)\geq C_{5}\;\sqrt{t}\;e^{-\frac{(\alpha+\varepsilon)^{2}}{t}}.

Proof: By the Markov property, Lemma 2, and (2.2),

𝐏0(Xsα,s[t/2,t])\displaystyle\!\!\!\!\!\!\!\!\!\!\!\!{\bf P}^{0}(X_{s}\leq-\alpha,\;\;\forall s\in[t/2,t])
=\displaystyle= αpt/2(y)𝐏y(Xsα,s[0,t/2])𝑑y\displaystyle\int_{-\infty}^{-\alpha}p_{t/2}(y)\;{\bf P}^{y}(X_{s}\leq-\alpha,\;\;\forall s\in[0,t/2])\;dy
\displaystyle\geq (α+ε)pt/2(y)𝐏y(Xsαs[0,t/2])𝑑y\displaystyle\int_{-\infty}^{-(\alpha+\varepsilon)}p_{t/2}(y)\;{\bf P}^{y}(X_{s}\leq-\alpha\;\;\forall s\in[0,t/2])\;dy
\displaystyle\geq C(α+ε)pt/2(y)𝑑y=C(α+ε)pt/2(y)𝑑y\displaystyle C\,\int_{-\infty}^{-(\alpha+\varepsilon)}p_{t/2}(y)\;dy=C\,\int_{(\alpha+\varepsilon)}^{\infty}p_{t/2}(y)\;dy
=\displaystyle= C1t(α+ε)ey2/t𝑑y\displaystyle C\;\frac{1}{\sqrt{t}}\int_{(\alpha+\varepsilon)}^{\infty}e^{-y^{2}/t}\;dy
=\displaystyle= C(α+ε)teξ2𝑑ξ\displaystyle C\int_{\frac{(\alpha+\varepsilon)}{\sqrt{t}}}^{\infty}e^{-\xi^{2}}\;d\xi
\displaystyle\geq Cte(α+ε)2t.\displaystyle C\sqrt{t}\;e^{-\frac{(\alpha+\varepsilon)^{2}}{t}}.

     


We can now prove Proposition 2. Fix α\alpha with 0<α<120<\alpha<\frac{1}{\sqrt{2}} and choose ε>0\varepsilon>0 so that ε2+(α+ε)2<12ε\varepsilon^{2}+(\alpha+\varepsilon)^{2}<\frac{1}{2}-\varepsilon. For this choice of ε\varepsilon, fix δ(0,1)\delta\in(0,1) appropriate for Lemmas 3, and 4. Suppose 0<t<δ0<t<\delta. We may apply Lemma 3 and Lemma 4, using the independence of XsX_{s} and YsY_{s}, to get

𝐏(TKαt)\displaystyle{\bf P}(T^{K_{\alpha}}\leq t) =\displaystyle= 𝐏(Zs(,α],for somes(0,t))\displaystyle{\bf P}(Z_{s}\in(-\infty,-\alpha],\;\;\hbox{for some}\;\;\;s\in(0,t))
\displaystyle\geq 𝐏0(Xsα,for alls[t/2,t])\displaystyle{\bf P}^{0}(X_{s}\leq-\alpha,\;\;\hbox{for all}\;\;\;s\in[t/2,t])
×𝐏0(Ys=0,for somes[t/2,t])\displaystyle\times\;\;{\bf P}^{0}(Y_{s}=0,\;\;\hbox{for some}\;\;\;s\in[t/2,t])
\displaystyle\geq Cteε2te(α+ε)2t.\displaystyle C\,t\,e^{-\frac{\varepsilon^{2}}{t}}\;e^{-\frac{(\alpha+\varepsilon)^{2}}{t}}.

On the other hand, it is proved in [McC85] that for all t>0t>0 and all positive integers n3n\geq 3,

(2.12) 𝐏(T𝔻t)c(n)ecos2(π/n)2t.{\bf P}(T^{\mathbb{D}}\leq t)\leq c(n)\;e^{-\frac{\cos^{2}(\pi/n)}{2t}}.

Fixing nn large enough, we see that for all t>0t>0,

(2.13) 𝐏(T𝔻t)Ce(12ε)t.{\bf P}(T^{\mathbb{D}}\leq t)\leq C\;e^{-\frac{(\frac{1}{2}-\varepsilon)}{t}}.

Thus,

limt0+𝐏(TKα<t)𝐏(T𝔻<t)limt0+Ctexp(ε2+(α+ε)2t)exp((12εt))=limt0+texp((12ε)(ε2+(α+ε)2)t)=.\lim_{t\longrightarrow 0^{+}}\frac{{\bf P}(T^{K_{\alpha}}<t)}{{\bf P}(T^{\mathbb{D}}<t)}\geq\lim_{t\to 0^{+}}\;C\;\frac{t\exp\left(-\frac{\varepsilon^{2}+(\alpha+\varepsilon)^{2}}{t}\right)}{\exp\left(-(\frac{\frac{1}{2}-\varepsilon}{t})\right)}=\lim_{t\to 0^{+}}\;t\;\exp\left(\frac{(\frac{1}{2}-\varepsilon)-(\varepsilon^{2}+(\alpha+\varepsilon)^{2})}{t}\right)=\infty.

     

Now we are ready for the proof of Theorem 1. By the scale invariance of Brownian motion we can assume that d(W)=1d(W)=1, and rotation invariance allows us to assume that αU-\alpha\in\partial U, where α=d(U)(0,12)\alpha=d(U)\in(0,\frac{1}{\sqrt{2}}). Then clearly 𝔻W{\mathbb{D}}\subseteq W, and although it is not necessarily true that UKαU\subseteq K_{\alpha}, we may still use our estimates for KαK_{\alpha} as a lower bound by the following lemma.

Lemma 5
𝐏(TU<t)12𝐏(TKα<t).{\bf P}(T^{U}<t)\geq\frac{1}{2}{\bf P}(T^{K_{\alpha}}<t).

Proof: Note that the complex conjugate of ZtZ_{t}, Z¯t\bar{Z}_{t}, is also a Brownian motion. Let

T~U=inf{t>0:Z¯tU}.\tilde{T}_{U}=\inf\{t>0:\bar{Z}_{t}\notin U\}.

We claim that TUT~UTKαT_{U}\wedge\tilde{T}_{U}\leq T_{K_{\alpha}} a.s. If not, then the union of Brownian paths

{Zt:0tTKα}{Z¯t:0tTKα}\{Z_{t}:0\leq t\leq T_{K_{\alpha}}\}\cup\{\bar{Z}_{t}:0\leq t\leq T_{K_{\alpha}}\}

would be a closed curve separating α-\alpha from \infty, and this contradicts simple connectivity. Thus,

𝐏(TUT~U<t)𝐏(TKα<t).{\bf P}(T_{U}\wedge\tilde{T}_{U}<t)\geq{\bf P}(T^{K_{\alpha}}<t).

But

𝐏(TUT~U<t)𝐏(TU<t)+𝐏(T~U<t)=2𝐏(TU<t),{\bf P}(T_{U}\wedge\tilde{T}_{U}<t)\leq{\bf P}(T_{U}<t)+{\bf P}(\tilde{T}_{U}<t)=2{\bf P}(T_{U}<t),

and the lemma follows.      


As for Theorem 1,

limt0+𝐏(TU<t)𝐏(TW<t)limt0+12𝐏(TKα<t)𝐏(T𝔻<t)=,\lim_{t\longrightarrow 0^{+}}\frac{{\bf P}(T^{U}<t)}{{\bf P}(T^{W}<t)}\geq\lim_{t\longrightarrow 0^{+}}\frac{\frac{1}{2}{\bf P}(T^{K_{\alpha}}<t)}{{\bf P}(T^{\mathbb{D}}<t)}=\infty,

completing the proof.

Remark. In fact, Lemma 5 holds with the constant 11 in place of 12\frac{1}{2}. However, the proof requires a number of results on symmetrization and polarization which are not related to the rest of this paper; for this reason we have given the simpler result and proof above, and postpone the proof of the stronger result until the end of Section 3.

2.2 Proof of Proposition 1

Let p(H(W),H(U))p\in({\rm H}(W),{\rm H}(U)) and δ=H(U)p2\delta=\frac{{\rm H}(U)-p}{2}. Since 𝐄[(TU)p+32δ]<{\bf E}[(T^{U})^{p+\frac{3}{2}\delta}]<\infty, the well-known Markov inequality (see e.g. [Fol13, 6.17]) implies

𝐏(TU>t)𝐄[(TU)p+32δ]tp+32δ.{\bf P}(T^{U}>t)\leq\frac{{\bf E}[(T^{U})^{p+\frac{3}{2}\delta}]}{t^{p+\frac{3}{2}\delta}}.

We now need a lower bound on 𝐏(TW>t){\bf P}(T^{W}>t). For that purpose we will use the so-called “layer cake” representation for the pp-th moment (see e.g. [Fol13, 6.24]):

(2.14) 𝐄((TW)p)=p0+tp1𝐏(TW>t)𝑑t.{\bf E}((T^{W})^{p})=p\int_{0}^{+\infty}t^{p-1}{\bf P}(T^{W}>t)dt.

We now claim that

lim supt+𝐏(TW>t)t(p+δ)=+,\limsup_{t\to+\infty}\frac{{\bf P}(T^{W}>t)}{t^{-(p+\delta)}}=+\infty,

since otherwise 𝐏(TW>t)t(p+δ)\frac{{\bf P}(T^{W}>t)}{t^{-(p+\delta)}} is bounded above by a constant, and then by (2.14) we get 𝐄[(TW)p]<+{\bf E}[(T^{W})^{p}]<+\infty which contradicts the definition of H(W){\rm H}(W). We obtain

lim supt+𝐏(TW>t)𝐏(TU>t)\displaystyle\limsup_{t\to+\infty}\frac{{\bf P}(T^{W}>t)}{{\bf P}(T^{U}>t)} \displaystyle\geq lim supt+𝐏(TW>t)t(p+δ)t(p+δ)tp+32δ𝐄[(TU)p+32δ]\displaystyle\limsup_{t\to+\infty}\frac{{\bf P}(T^{W}>t)}{t^{-(p+\delta)}}\;\frac{t^{-(p+\delta)}\;t^{p+\frac{3}{2}\delta}}{{\bf E}[(T^{U})^{p+\frac{3}{2}\delta}}]
=\displaystyle= lim supt+𝐏(TW>t)t(p+δ)tδ2𝐄[(TU)p+32δ]=+,\displaystyle\limsup_{t\to+\infty}\frac{{\bf P}(T^{W}>t)}{t^{-(p+\delta)}}\;\frac{t^{\frac{\delta}{2}}}{{\bf E}[(T^{U})^{p+\frac{3}{2}\delta}]}=+\infty,

which ends the proof.      

3 Concluding remarks

As was discussed in the Introduction, Theorem 1 shows that the unit disk is not extremal among Schlicht domains for fast exits. In fact, many Schlicht domains, including the Koebe domain and the half-plane {Re(z)12}\{{\rm Re}(z)\geq-\frac{1}{2}\}, have a higher probability of fast exits. We do not know if the constant 12\frac{1}{\sqrt{2}} in Theorem 1 is optimal; it would be nice to know what is the best possible. As mentioned in the introduction, there is reason to suspect that the best possible constant is even 1. To see this, let Hα={Re(z)<α}H_{\alpha}=\{{\rm Re}(z)<\alpha\}. We then have the following proposition.

Proposition 3

Let W be a simply connected domain with 0W0\in W. Suppose that d(W)=1d(W)=1, and 0<α<10<\alpha<1. Then, for all sufficiently small t>0t>0,

(3.1) 𝐏(THα<t)>𝐏(TW<t).{\bf P}(T^{H_{\alpha}}<t)>{\bf P}(T^{W}<t).

In fact,

limt0+𝐏(THα<t)𝐏(TW<t)=.\lim_{t\longrightarrow 0^{+}}\frac{{\bf P}(T^{H_{\alpha}}<t)}{{\bf P}(T^{W}<t)}=\infty.

Proof: (sketch) By projection onto the real part, the exit time of HαH_{\alpha} has the same distribution as τα\tau_{\alpha}, the first hitting time of α\alpha by a one-dimensional Brownian motion, as defined at the beginning of Section 2. It is then straightforward to show using the Gaussian density that 𝐏(THα<t)Ceα2(1+ε)2t{\bf P}(T^{H_{\alpha}}<t)\geq Ce^{\frac{-\alpha^{2}(1+\varepsilon)}{2t}} for tt sufficiently small and any 1>ε>01>\varepsilon>0. On the other hand, 𝔻W{\mathbb{D}}\subseteq W, and from (2.13) we therefore have 𝐏(TWt)Ce(1ε)2t{\bf P}(T^{W}\leq t)\leq C\;e^{-\frac{(1-\varepsilon)}{2t}} for tt sufficiently small and any 1>ε>01>\varepsilon>0. The result follows by choosing ε\varepsilon small enough so that α2(1+ε)<1ε\alpha^{2}(1+\varepsilon)<1-\varepsilon.      

Remark: Naturally, this result holds with HαH_{\alpha} replaced by any domain contained in a rotation of HαH_{\alpha}.

The moments of the exit time have been considered previously by several authors. In [Bur77], it is shown that for the wedge Rθ={|Arg(z)|<θ}R_{\theta}=\{|{\rm Arg}(z)|<\theta\}, that is, the infinite wedge centered at the positive real axis of angular width 2θ2\theta, we have 𝐄[(TRθ)p]<{\bf E}[(T^{R_{\theta}})^{p}]<\infty if and only if p<π4θp<\frac{\pi}{4\theta}, so

(3.2) H(Rθ)=π4θ.{\rm H}(R_{\theta})=\frac{\pi}{4\theta}.

A domain WW is spiral-like of order σ0\sigma\geq 0 with center aa if, for any zWz\in W, the spiral {a+(za) exp(teiσ):t0}\{a+(z-a)\mbox{ exp}(te^{-i\sigma}):t\leq 0\} also lies within WW; WW is star-like if it is spiral-like of order σ=0\sigma=0. The quantity H(W){\rm H}(W) can be determined explicitly if WW is star-like or spiral-like, as is shown in [Mar15], with equivalent analytic results appearing in [Han70] and [Han71]. In particular, if we take a=0a=0 then, since WW is spiral-like, the quantity

(3.3) 𝒜r,W=max{m(E):E is a subarc of W{|z|=r}},{\cal A}_{r,W}=\max\{m(E):E\mbox{ is a subarc of }W\cap\{|z|=r\}\},

is non-increasing in rr (here mm denotes angular Lebesgue measure on the circle). We may therefore let 𝒜W=limr𝒜r,W{\cal A}_{W}=\lim_{r\nearrow\infty}{\cal A}_{r,W}, and then [Mar15, Thm. 2] we have H(W)=π2𝒜Wcos2σH(W)=\frac{\pi}{2{\cal A}_{W}\cos^{2}\sigma}.

As mentioned before we suspect that in many cases the lim sup\limsup in Proposition 1 is not necessary, and venture the following conjecture.

Conjecture 1

Suppose that H(U)>H(W){\rm H}(U)>{\rm H}(W) and WW is spiral-like. Then

(3.4) limt𝐏(TW>t)𝐏(TU>t)=.\lim_{t\longrightarrow\infty}\frac{{\bf P}(T^{W}>t)}{{\bf P}(T^{U}>t)}=\infty.

Note that this includes the case that WW is star-like, as well as the wedge RθR_{\theta}. This conjecture would follow from the following, if true.

Conjecture 2

Suppose that WW is spiral-like. Then for any p>H(W)p>{\rm H}(W) there is a constant C>0C>0 so that 𝐏(TW>t)Ctp{\bf P}(T_{W}>t)\geq\frac{C}{t^{p}}.

Our evidence for the truth of Conjecture 1 is at follows. First note that Markov’s inequality, used as in Proposition 1, yields the following fact.

Proposition 4

For any p<H(U)p<{\rm H}(U), there is a constant C>0C>0 so that 𝐏(TU>t)Ctp{\bf P}(T^{U}>t)\leq\frac{C}{t^{p}}.

Furthermore the bound required is true in the lim sup\limsup sense, as is shown in the proof of Proposition 1. Next we prove Conjecture 1 when WW is a half-plane or quarter-plane. Let W={Re(z)<1}W=\{{\rm Re}(z)<1\}; recall from (3.2) that H(W)=12{\rm H}(W)=\frac{1}{2}. Then, using the reflection principle, 𝐏(TU>t){\bf P}(T^{U}>t) can be bounded below as follows.

𝐏0(Xs<1,s[0,t])\displaystyle\!\!\!\!\!\!\!\!\!\!\!\!{\bf P}^{0}(X_{s}<1,\;\;\;\forall s\in[0,t])
=\displaystyle= 12𝐏0(Xt1)=1212πt1ex2/2t𝑑x\displaystyle 1-2{\bf P}^{0}(X_{t}\geq 1)=1-2\frac{1}{\sqrt{2\pi t}}\int_{1}^{\infty}e^{-x^{2}/2t}\;dx
=\displaystyle= 2π012teξ2𝑑ξCt.\displaystyle\frac{2}{\sqrt{\pi}}\int_{0}^{\frac{1}{\sqrt{2t}}}e^{-\xi^{2}}\;d\xi\geq\frac{C}{\sqrt{t}}.

Now suppose W={Re(z)>0,Im(z)>0}W=\{{\rm Re}(z)>0,{\rm Im}(z)>0\}; recall from (3.2) that H(W)=1{\rm H}(W)=1. Then, using the independence of the one dimensional components of planar Brownian motion, and the calculation for the half-plane, we have

𝐏1+i(TW>t)\displaystyle{\bf P}^{1+i}(T^{W}>t) =\displaystyle= 𝐏1(Xs>0,s[0,t])2Ct\displaystyle{\bf P}^{1}(X_{s}>0,\;\;\;\forall s\in[0,t])^{2}\geq\frac{C}{t}

for tt bounded away from 0. Finally, we prove an improvement of Lemma 5, as we promised in Section 2.

Proposition 5

Let UU be a simply connected domain with 0U0\in U and let α=d(U)(0,)\alpha=d(U)\in(0,\infty). If Kα=(,α]K_{\alpha}={\mathbb{C}}\setminus(-\infty,-\alpha], then for every t>0t>0,

(3.6) 𝐏(TU>t)𝐏(TKα>t).{\bf P}(T^{U}>t)\leq{\bf P}(T^{K_{\alpha}}>t).

Proof: Let pU(t,0,w)p^{U}(t,0,w) be the transition density function for Brownian motion killed upon hitting U\partial U. Then (see e.g. [CZ12, Theorem 2.4])

(3.7) 𝐏(TU>t)=UpU(t,0,w)A(dw),   0<t<+,{\bf P}(T^{U}>t)=\int_{U}p^{U}(t,0,w)\;A(dw),\;\;\;0<t<+\infty,

where AA denotes the area measure. A similar formula holds for 𝐏(TKα>t){\bf P}(T^{K_{\alpha}}>t). Therefore, it suffices to prove that

(3.8) UpU(t,0,w)A(dw)UpKα(t,0,w)A(dw),   0<t<+.\int_{U}p^{U}(t,0,w)\;A(dw)\leq\int_{U}p^{K_{\alpha}}(t,0,w)\;A(dw),\;\;\;0<t<+\infty.

We will prove (3.8) using the theory of polarization and symmetrization. We refer to [Hay94], [Dub14], [BS00] for the definitions and basic facts.

The function pU(t,0,w)p^{U}(t,0,w) satisfies the heat equation on UU. Let PHUP_{H}U denote the polarization of UU with respect to a half-plane HH with 0H0\in\partial H. Then [BS00, Theorem 9.4]

(3.9) pU(t,0,w)+pU(t,0,RHw)pPHU(t,0,w)+pPHU(t,0,RHw),   0<t<,p^{U}(t,0,w)+p^{U}(t,0,R_{H}w)\leq p^{P_{H}U}(t,0,w)+p^{P_{H}U}(t,0,R_{H}w),\;\;\;0<t<\infty,

where RHwR_{H}w denotes the reflection of ww in the line H\partial H. It follows from (3.9) that for every r(0,+)r\in(0,+\infty),

(3.10) 02πpU(t,0,reiθ)𝑑θ02πpPHU(t,0,reiθ)𝑑θ,   0<t<.\int_{0}^{2\pi}p^{U}(t,0,re^{i\theta})d\theta\leq\int_{0}^{2\pi}p^{P_{H}U}(t,0,re^{i\theta})d\theta,\;\;\;0<t<\infty.

By a standard technique involving a sequence of polarizations, (3.10) leads to the inequality

(3.11) 02πpU(t,0,reiθ)𝑑θ02πpU(t,0,reiθ)𝑑θ,   0<t<, 0<r<,\int_{0}^{2\pi}p^{U}(t,0,re^{i\theta})d\theta\leq\int_{0}^{2\pi}p^{U^{*}}(t,0,re^{i\theta})d\theta,\;\;\;0<t<\infty,\;0<r<\infty,

where UU^{*} is the circular symmetrization of UU with respect to the positive semi-axis. Since UKαU^{*}\subset K_{\alpha}, we have

(3.12) 02πpU(t,0,reiθ)𝑑θ02πpKα(t,0,reiθ)𝑑θ,   0<t<, 0<r<.\int_{0}^{2\pi}p^{U^{*}}(t,0,re^{i\theta})d\theta\leq\int_{0}^{2\pi}p^{K_{\alpha}}(t,0,re^{i\theta})d\theta,\;\;\;0<t<\infty,\;0<r<\infty.

By (3.11), (3.12), and integration over r(0,)r\in(0,\infty), we obtain (3.8).      

References

  • [BS00] F. Brock and A. Solynin. An approach to symmetrization via polarization. Transactions of the American Mathematical Society, 352(4):1759–1796, 2000.
  • [Bur77] D. Burkholder. Exit times of Brownian motion, harmonic majorization, and Hardy spaces. Advances in Mathematics, 26(2):182–205, 1977.
  • [CZ12] K. Chung and Z. Zhao. From Brownian motion to Schrödinger’s equation, volume 312. Springer Science & Business Media, 2012.
  • [Dav79] B. Davis. Brownian motion and analytic functions. The Annals of Probability, 7(6):913–932, 1979.
  • [Dub14] V. Dubinin. Condenser capacities and symmetrization in geometric function theory. Springer, 2014.
  • [Dur84] R. Durrett. Brownian motion and martingales in analysis. Wadsworth Advanced Books & Software, 1984.
  • [Fol13] G. Folland. Real analysis: modern techniques and their applications. John Wiley & Sons, 2013.
  • [Han70] L. Hansen. Hardy classes and ranges of functions. The Michigan Mathematical Journal, 17(3):235–248, 1970.
  • [Han71] L. Hansen. The Hardy class of a spiral-like function. The Michigan Mathematical Journal, 18(3):279–282, 1971.
  • [Hay94] W. Hayman. Multivalent functions, volume 110. Cambridge University Press, 1994.
  • [Mar15] G. Markowsky. The exit time of planar Brownian motion and the Phragmén–Lindelöf principle. Journal of Mathematical Analysis and Applications, 422(1):638–645, 2015.
  • [McC85] T. McConnell. The size of an analytic function as measured by Lévy’s time change. The Annals of Probability, 13(3):1003–1005, 1985.