6. Scaling limits of labeled maps
6.1. Continuum measures on labeled maps
Let (E, d) be a metric space. We letC(E) be the set ofE-valued continuous paths, i.e.
of continuous functionsf: [0, ζ]!R, for some ζ=ζ(f)0 called the duration off. This space is endowed with the distance
dist(f, g) = sup
t0
d(f(t∧ζ(f)), g(t∧ζ(g)))+|ζ(f)−ζ(g)|,
that makes it a Polish space ifE is itself Polish. We let ˆf(t)=f(ζ(f)−t), 0tζ(f), be the time-reversed function off∈C(E). Sometimes, we will also have to consider contin-uous functionsf:R+!E with infinite duration, so we letC(E) be the set of continuous functions with finite or infinite duration. We endow it with a topology such thatfn!f in C(E) if and only if ζ(fn)!ζ(f) and fn(· ∧ζ(fn)) converges tof(· ∧ζ(f)) uniformly over compact subsets ofR+.
We let CLM(k+1) be the set of pairs of the form (s,(We)e∈E(s)), where s is a schema withk+1 faces, and for everye∈E(s),Weis an element ofC(C(R)), i.e. for every s∈[0, ζ(We)],We(s) is a function inC(R) that can be written
(We(s, t),0tζ(We(s))).
The space CLM(k+1) is a Polish space, a complete metric being for instance the one letting (s,(We)e∈E(s)) and (s,(We)e∈E(s)) be at distance 1 if s=s, and at distance maxe∈E(s)dist(We, We) ifs=s.
In order to make clear distinctions between quantities likeζ(We) andζ(We(s)), we will adopt the following notation in the sequel. Fore∈E(s) we let
Me=We(0)∈ C(R), re=ζ(Me) and σe=ζ(We). (17) The measure that will play a central role is a continuum measure CLM(k+1) on labeled maps. To define it, we first need to describe the continuum analogs of the walks and discrete snakes considered in the previous section.
6.1.1. Bridge measures
In the sequel, we let (Xt, t∈[0, ζ(X)]) be the canonical process on the space C(R) of R-valued continuous functions with finite or infinite duration. The infimum process of X is the processX defined by
Xt= inf
0stXs, 0tζ(f).
For allx∈R, we also letTx=inf{t0:Xt=x}∈[0,∞] be the first hitting time ofxbyX. The building blocks of CLM(k+1) are going to be measures on different classes of paths. We let Px be the law of standard 1-dimensional Brownian motion started from x, and for y <xwe let E(y,∞)x be the law of (Xt,0tTy) under Px, that is, Brownian motion killed at first exit time of (y,∞).
Next, let Ptx!y be the law of the 1-dimensional Brownian bridge fromxto y with durationt:
Ptx!y(·) =Px((Xs)0st∈ · |Xt=y).
A slick way to properly define this singular conditioned measure is to letPtx!y be the law of (Xs+(y−Xt)s/t,0st) underPx, see [32, Chapter I.3]. We also letPtx be the law of thefirst-passage Brownian bridgefromxto 0 with durationt, defined formally by
Ptx(·) =Px((Xs,0st)∈ · |T0=t).
Regular versions for this singular conditioning can be obtained using space-time Doob h-transforms. We refer to [2] for a recent and quite complete treatment of first-passage bridges and limit theorems for their discrete versions, which will be helpful to us later on.
Fort>0 let
pt(x, y) =Px(Xt∈dy)
dy = 1
√2πte−(x−y)2/2t, x, y∈R,
¯
pt(x,0) =Px(T0∈dt)
dt = x
√2πt3e−x2/2t, x >0.
Thebridge measure from xto yis then theσ-finite measure Bx!y defined by Bx!y(dX) =
∞
0
dt pt(x, y)Ptx!y(dX).
We also let B+x!y be the restriction of Bx!y to the set C+(R)={f∈C(R):f0}. The reflection principle entails thatPtx!y(C+(R))=1−e−2xy/t, and it is a simple exercise to show that for x, y >0, letting p+t(x, y)=pt(x, y)−pt(x,−y)=Px(Xt∈dy, Xt0)/dy, one has
B+x!y(dX) = ∞
0
dt p+t(x, y)Ptx!y(dX| C+(R)), (18) We now state two path decomposition formulas for the measuresBx!y that will be useful for our later purposes. Ifμis a measure onC(R), we let ˆμ be the image measure ofμ under the time-reversal operationf!fˆ. If μ andμ are two probability measures onC(R) such thatμ(X(ζ(X))=z)=μ(X(0)=z)=1 for somez∈R, then we letμμ be the image measure ofμ⊗μ under the concatenation operation (f, g)!f gfrom C(R)2 toC(R), where
(f g)(t) =
f(t), if 0tζ(f),
g(t−ζ(f)), ifζ(f)tζ(f)+ζ(g).
Theagreement formula of [30, Corollary 3] states that Bx!y(dX) =
x∧y
−∞
dz(E(z,∞)x E(z,∞)y )(dX). (19) In particular,
B+x!y(dX) = x∧y
0
dz(E(z,∞)x E(z,∞)y )(dX).
This decomposition should be seen as one of the many versions of Williams’ decomposi-tions formulas for Brownian paths: Here, the variablez plays the role of the minimum of the generic pathX.
A second useful path decomposition states that, forx, y∈R,
R
dz(Bx!zBz!y)(dX) =ζ(X)Bx!y(dX). (20) To see this, write
ζ(X)Bx!y(dX) = ∞
0
dr pr(x, y) r
0
dsPrx!y(dX), then note that, for everyrs0,
Prx!y(Xs∈dz) =ps(x, z)pr−s(z, y) pr(x, y) dz,
and use the fact that, givenXs=z, the paths (Xu,0us) and (Xs+u,0ur−s) under Prx!y are independent Brownian bridges, by the Markov property.
6.1.2. Brownian snakes
LetW be the canonical process onC(C(R)). That is to say, for everys∈[0, ζ(W)] (or every s0 ifζ(W)=∞),W(s) is an element ofC(R). For simplicity, we letζs=ζ(W(s)) be the duration of this path, and letW(s, t)=W(s)(t) for 0tζs. The process (ζs,0sζ(W)) is called thelifetime process ofW, we also say thatW isdrivenby (ζs,0sζ(W)). In order to clearly distinguish the duration ofW with that ofW(s) for a givens, we will rather denote the durationζ(W) ofW by the letterσ(W), consistently with (17).
Let us describe the law of Le Gall’s Brownian snake (see [14] for an introduction to the subject). Conditionally given the lifetime process (ζs), the process W under the Brownian snake distribution is a non-homogeneous Markov process with the following transition kernel. Given W(s)=(w(t),0tζt), the law of the pathW(s+s) is that of the path (w(t),0tζs+s) defined by
w(t) =
W(s, t), if 0tζˇs,s+s,
W(s,ζ(s, s+sˇ ))+Bt−ζ(s,s+sˇ ), if ˇζs,s+stζs+s, where (Bt, t0) is a standard Brownian motion independent ofζ, and
ζˇs,s+s= inf
sus+sζu.
We letQw be the law of the process W started from the pathw∈C(R), and driven by a Brownian motion started fromζ(w) and killed at first hitting of 0 (i.e. a process with lawE(0,∞)ζ(w) ).
For our purposes, the key property of the Brownian snake will be the following representation using Poisson random measures, which can be found for instance in [14]
and [21]. Namely, let w∈C(R). Recall that (ζs,0sσ(W)) is the lifetime process driving the canonical process W, and that under Qw we have W(0)=w and ζ0=ζ(w).
For 0rζ0, define
Σr= inf{s0 :ζs=ζ0−r},
so in particular σ=Σζ0, and the process (Σr,0rζ0) is non-decreasing and right-continuous. For everyr∈[0, ζ0] such that Σr>Σr−, let
W(r)(s) = (W(Σr−+s, t+r),0tζΣr−+s−r), 0sΣr−Σr−. (21) In this way, every W(r) is an element of C(C(R)). Then, under Qw, we consider the measure
Mw=
0rζ(w)
δ(r,W(r))1{Tr>Tr−}.
Lemma 5 in [14, Chapter V] states that, underQw, the measureMwis a Poisson random measure on [0, ζ(w)]×C(R), with intensity measure given by
2dr1[0,ζ(w)](r)Nw(r)(dW). (22)
Here, the measureNx is called theItˆo excursion measure of the Brownian snake started at x. It is the σ-finite “law” of the Brownian snake started from the (trivial) pathw with w(0)=x and ζ(w)=0, and driven by a trajectory which is a Brownian excursion under the Itˆo measure of the positive excursions of Brownian motion n(dζ). See [32, Chapter XII] for the properties of the excursion measuren, which is denoted by n+ in this reference. It is important that we fix the normalization of this measure so that the factor 2 in (22) makes sense, and we choose it so thatn(supζ >x)=(2x)−1for everyx>0.
6.1.3. The measureCLM(k+1)
For every dominant schemas∈S(k+1)d , we letλsbe the measure onRV(s)defined by λs(d(v)v∈V(s)) =
v∈VN(s)
δ0(dv)
v∈VI(s)
dv1{v>0}
v∈VO(s)
dv,
called the Lebesgue measure on admissible labelings of s. We define the continuum measureCLM(k+1) onCLM(k+1)by
CLM(k+1)(d(s,(We)e∈E(s))) =S(k+1)d (ds)
RV(s)
λs(d(v)v∈V(s))
×
e∈EN(s)
C+(R)E(0,∞)e
− (dMe)QMe(dWe)QMe(dWe¯)
×
e∈EI(s)
C+(R)B+e
−!e+(dMe)QMe(dWe)QMe(dWe¯)
×
e∈EO(s)
C(R)Be−!e+(dMe)QMe(dWe)QMe(dW¯e), (23) where S(k+1)d is the counting measure on S(k+1)d . Note that, since EN(s), EI(s) and EO(s) partitionE(s), and since we have fixed an orientation convention for the edges in these sets, the oriented edges e and ¯e exhaust E(s) when e varies along EN(s), EI(s) andEO(s).
We also define a measureCLM(k+1)1 , which is roughly speaking a conditioned version ofCLM(k+1) given
e∈E(s)σe=1. Contrary toCLM(k+1), this is a probability measure on CLM(k+1). Its description is more elaborate than CLM(k+1), because it does not have a simple product structure. It is more appropriate to start by defining the trace of CLM(k+1)1 on (s,(v),(re)), i.e. its push-forward by the mapping (s,(We))!(s,(v),(re)), wherere=ζ(We(0)) andv=We(0,0) wheneverv=e−. This trace is
1
Υ(k+1)S(k+1)d (ds)λs((dv)v∈V(s))
e∈E(s)
drep(e)re (e−, e+)
¯
p1(2r,0), (24)
where Υ(k+1)∈(0,∞) is the normalizing constant making it a probability distribution, andp(e)r (x, y) is defined by
• p(e)r (x, y)=pr(x, y) ife∈EO(s),
• p(e)r (x, y)=p+r(x, y) ife∈EI(s),
• p(e)r (x,0)= ¯pr(x,0) ife∈EN(s) (entailing automaticallye+=0).
Finally, in the last displayed expression, we letr=
e∈E(s)re. Then, conditionally given (s,(v),(re)), the processes (Me, e∈E(s)) are independent, and respectively
• Me has lawPree
−!e+ ife∈EO(s),
• Me has lawPree
−!e+(· |C+(R)) ife∈EI(s),
• Me has lawPree
− ife∈EN(s).
As usual, the processes (M¯e, e∈E(s)) are defined by time-reversal: M¯e=Me. Fi-nally, conditionally given (Me, e∈E(s)), the processes (We, W¯e, e∈E(s)) are independent Brownian snakes respectively started fromMe and Me, conditioned on the event that the sumσof their durations is equal to 1.
This singular conditioning is obtained as follows. First consider a Brownian snake (W (s),0s1) with lifetime process given by a first-passage bridge (ζ(s),0s1) from
2r=2
e∈E(s)re to 0 with duration 1, and such thatW (0) is the constant path 0 with duration 2r. Lete1, ..., e4k−3be an arbitrary enumeration ofE(s), and letri=re1+...+rei for everyi∈{0,1, ...,4k−3}, with the convention thatr0=0. Then, let
i= inf{s0 :ζ(s) = 2r−ri}, and for 1i4k−3, let
Wei(s, t) =W (s+ i−1, t+2r−ri), 0s i− i−1, 0tζ(s+ i−1)−ζ( i). (25) The processes (We, e∈E(s)) are then independent Brownian snakes started from the constant trajectories 0 with respective durations re, and conditioned on having total durationσ=
eσeequal to 1. We finally letζe be the lifetime process ofWe, and We(s, t) =Me
t∧ inf
0usζe(u)
+We(s, t), 0sσe, 0tζe(s), (26) that is, we change the initial value ofWetoMe. The different processes (We, e∈E(s)) are then conditionally independent snakes started respectively fromMe, conditioned onσ=1.
Hence, the snakes (We, e∈E(s)) can be obtained by “cutting into bits of initial lengthsre” a snake with lifetime process having lawE(0,∞)2r [· |T0=1] and started from the constant zero trajectory, to which we superimpose (independently) the initial trajectoriesMe.
The relation betweenCLM(k+1)andCLM(k+1)1 goes as follows. For everyc>0 define a scaling operation Ψ(k+1)c on CLM(k+1), sending (s,(We)e∈E(s)) to (s,(We[c])e∈E(s)), where
We[c](s, t) =c1/4We s c, t
c1/2
, 0scσe, 0tc1/2ζe(s/c).
Then, we letCLM(k+1)c be the image measure ofCLM(k+1)1 by Ψ(k+1)c . Sometimes we will abuse notation and write Ψ(k+1)c ((We)e∈E(s)) instead of (We[c])e∈E(s).
Proposition 24. We have
CLM(k+1)= Υ(k+1) ∞
0
dσ σk−9/4CLM(k+1)σ .
Proof. The idea is to disintegrate formula (23) with respect toσ=
e∈E(s)σe. Note that conditionally given s and (Me, e∈E(s)), the snakes (We)e∈E(s) are independent Brownian snakes started respectively from the trajectories M¯e, so that their lifetime processes are independent processes with respective lawsE(0,∞)re (dζe). In particular, the lifetime ofWeis an independent variable with the same law asT−re, the first hitting time of−reunder P0. From this, we see that (still conditionally givensand (Me, e∈E(s))), the total lifetime σ has the same distribution as T−2r under P0, where r=
e∈E(s)re, and this has a distribution given by ¯pσ(2r,0)dσ (in several places in this proof, we will not distinguish the random variableσ from its generic value, and will do a similar abuse of notation for the elements (v) and (re), to avoid introducing new notation).
Consequently, we obtain that the trace ofCLM(k+1) on (s,(v),(re), σ) equals are independent. Finally, the paths We are independent Brownian snakes respectively starting fromMe¯, conditioned on the sum of their durations beingσ. These paths can be defined by applying the scaling operator Ψ(k+1)σ to a family of independent Brownian snakes started from the paths (σ−1/4Me(σ1/2t),0tσ−1/2re), and conditioned on the sum of their durations being 1.
Let us change variables re=σ−1/2re and v=σ−1/4v and let r=
and, conditionally given these quantities, the processes (Me) are chosen as in the previous paragraph, while the snakes (We) are the images under Ψ(k+1)σ of independent snakes
respectively started from Me¯, conditioned on the sum of their durations being 1. We then use the fact that, for allr, c>0 and allx, y∈Rsuch that the following expressions make sense, we have
pcr(c1/2x, c1/2y) =c−1/2pr(x, y), p+cr(c1/2x, c1/2y) =c−1/2p+r(x, y), and
¯
pcr(c1/2x,0) =c−1p+r(x,0),
which is a simple consequence of Brownian scaling. Finally, for dominant schemata, we have 3k−2 vertices, among which k are in VN(s) and do not contribute to λs(d(v)), and 4k−3 edges among which 2k are inEN(s), yielding that the trace of CLM(k+1) on (s,(v),(re), σ) is
σ2(k−1)/4+(2k−3)/4−1dσS(k+1)d (ds)λs(d(v)v∈V(s))
e∈E(s)
drep(e)re(e−, e+)
¯ p1(2r,0), and we recognize Υ(k+1)σk−9/4dσ times the trace of CLM(k+1)1 on (s,(v),(re)). Since CLM(k+1)σ is the image measure ofCLM(k+1)1 by Ψ(k+1)σ , we have finally obtained that the trace of CLM(k+1) on the variable σ is Υ(k+1)σk−9/4dσ, and that CLM(k+1)σ , σ >0, are conditional measures ofCLM(k+1)givenσ, as wanted.