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The Brownian zero set has no isolated points

Statement

Assume the Axiom of Choice. Let B be standard Brownian motion and let Z be the zero set of the everywhere-continuous, zero-start representative X fixed in The Brownian zero set. On one measurable event of probability one, every t in Z is a limit point of Z{t}. Thus Z is closed and has no isolated points in [0,infinity). On the supplied common full event, this assertion also holds for the original B path.

Facts & Assumptions

Given: AC, B, its normalized representative X, and its zero set Z.

[F1]

X has measurable coordinates, every path is continuous and starts at zero, and X agrees with B on a measurable full event. Thus its finite-dimensional distributions, including the independent Gaussian increments, agree with those of B and X itself is a standard Brownian motion. Z is closed and contains zero. The Brownian zero set Brownian motion

[F2]

Form the raw natural filtration and usual augmentation of X itself. This augmentation contains the raw filtration of X. The proof applies the strong Markov theorem directly to X and requires no identification with the filtration of the original B. Natural and usual augmented Brownian filtrations

[F3]

Stopping times use the non-strict sublevel test. For a bounded stopping time S of the usual augmentation of a Brownian motion X and a bounded product-measurable functional G, the conditional future identity is E[G((XS+r)r0)GS]=Ψ(XS), where Ψ(x)=G(x+w)μ(dw) and mu is Wiener measure. Since X is everywhere continuous and S finite, the theorem's measurable random-time version equals the literal evaluation everywhere. Continuous-time stopping times and stopped sigma-algebras Strong Markov property of Brownian motion Wiener measure on continuous path space

[F4]

The maximum of a continuous zero-start Brownian motion on [0,h], h>0, has distribution P(Mhx)=2Φ(x/h)1 for x>=0; in particular P(M_h=0)=0. The process -X is also Brownian by negating its independent centered Gaussian increments. Law of the Brownian maximum Brownian motion

[F5]

Rational density and countable subadditivity for null events. The rationals embed densely in the reals Basic identities for a probability measure

[F6]

Conditional-expectation equalities are almost-sure equalities of versions characterized by event integrals. Full AC is inherited from the Brownian, completion and conditional-expectation interfaces. Conditional expectation as an ae class The Axiom of Choice

Proof

technique · direct
1.1

Apply [F4] to X and -X on each interval [0,1/m], m>=1. Off a countable union of null events [F5], X has both a positive and a negative value on each such interval. Neither value is at zero. The intermediate value theorem between those two times gives a zero at a positive time <=1/m. Therefore positive zeros accumulate at zero. The same argument applies to the coordinate process under Wiener measure, which is Brownian by [F3].

F1F3F4F5
1.2

Fix a nonnegative rational q and define τq=inf{sq:Xs=0}, with infimum of the empty set infinity. For t<q its sublevel event is empty. For t>=q, compactness and closedness give {τqt}={minr[q,t]Xr=0}, which equals the event that the infimum over (Q[q,t]){q,t} is zero. At t=q this is the singleton test X_q=0. Every coordinate used has time <=t, so the event belongs to the raw filtration of X and thus to its usual augmentation [F2]. This proves that tau_q is a stopping time. If tau_q is finite, closedness gives X_{tau_q}=0. No recurrence or finiteness assertion is needed.

F1F2F3F5
2.1

Define G(w)=1m1k>m{infrQ[1/k,1/m]w(r)=0}. This is product measurable, since only countably many coordinates and set operations occur. For continuous w the compact infimum is a minimum and equals the rational infimum, so G(w)=1 precisely when positive zeros accumulate at zero. Step 1.1 shows Ψ(0)=G(w)μ(dw)=1. For any nonzero x, continuity of w at zero and w(0)=0 exclude zeros of x+w sufficiently near zero; hence Ψ(x)=0. These assertions hold mu-almost surely even if a realization of Wiener measure contains nonzero-start null paths.

F1F3F5step 1.1
3.1

For integers N>=1 put S=tau_q wedge N. It is a bounded stopping time: its sublevel event is {tau_q<=t} for t<N and the whole space for t>=N. By [F3] and step 2.1, E[G((XS+r)r0)GS]=1{XS=0}. The event {X_S=0} is in the stopped sigma-algebra by [F3]. Integrating the nonnegative variable 1{XS=0}(1G((XS+r))) over the whole space gives zero by the conditional event-integral identity [F6]; since this variable is an indicator, the event it indicates is null. Thus, almost surely on {X_S=0}, positive zeros of the post-S path accumulate at zero. On {tau_q<=N} step 1.2 gives S=tau_q and X_S=0. We conclude that, outside a measurable null event, whenever tau_q<=N the zero tau_q is approached by other zeros from the right.

F3F6step 2.1step 1.2
4.1

Take the intersection of the full event in step 1.1 with the full events in step 3.1 over the countable set of nonnegative rationals q and positive integers N. It is a measurable full event by [F5]. On it, every finite tau_q is approached by other zeros from the right, since some N is larger than tau_q.

F5step 1.1step 3.1
5.1

On that event zero is not isolated by step 1.1. If t>0 were an isolated zero, some delta with 0<delta<t would satisfy Z(tδ,t+δ)={t}. By rational density choose q in (t-delta,t). There are no zeros in [q,t), and t is a zero, so tau_q=t. Step 4.1 supplies other zeros in (t,t+delta), a contradiction. Hence every zero is a limit point of the other zeros. Z is already closed by [F1].

F1F5step 1.1step 4.1
6.1

The empty hitting set and tau_q=infinity cause no problem because only bounded S are used, and the conclusion about tau_q is conditional on its finiteness. The time-zero endpoint has its own full event. The only intersections of full events are countable; no rational time is claimed itself to be a positive Brownian zero. On the common agreement event in [F1] the original B and X have identical zero sets, so the original path assertion follows there. AC is inherited as in [F6]. In particular, the proof never places an arbitrary normalization null event in the original Brownian filtration.

F1F6step 1.2step 4.1step 5.1

Source notes

The standard rational-next-zero and strong-Markov architecture is described in Durrett, Section 7.4.1, and Sousi, Theorem 6.39. Here immediate zero accumulation follows from the maximum law applied to both signs. Using tau_q wedge N removes the need for a recurrence argument. The auxiliary filtration is the usual augmentation of the normalized Brownian motion itself.

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