El e c t ro n ic
Jo ur
n a l o
f P
r o
b a b il i t y
Vol. 14 (2009), Paper no. 90, pages 2580–2616. Journal URL
http://www.math.washington.edu/~ejpecp/
Recurrence and transience for long range reversible
random walks on a random point process
∗
Pietro Caputo
†Alessandra Faggionato
‡Alexandre Gaudillière
§Abstract
We consider reversible random walks in random environment obtained from symmetric long– range jump rates on a random point process. We prove almost sure transience and recurrence results under suitable assumptions on the point process and the jump rate function. For recur-rent models we obtain almost sure estimates on effective resistances in finite boxes. For transient models we construct explicit fluxes with finite energy on the associated electrical network.
Key words: random walk in random environment, recurrence, transience, point process, elec-trical network.
AMS 2000 Subject Classification:Primary 60K37; 60G55; 60J45.
Submitted to EJP on January 23, 2009, final version accepted November 3, 2009.
∗This work was supported by the European Research Council through the “Advanced Grant” PTRELSS 228032 and by
the GREFI-MEFI
†Dip. Matematica, Università di Roma Tre, L.go S. Murialdo 1, 00146 Roma, Italy. e–mail: caputo@mat.uniroma3.it ‡Dip. Matematica, Università di Roma “La Sapienza”, P.le Aldo Moro 2, 00185 Roma, Italy. e–mail:
fag-giona@mat.uniroma1.it
1
Introduction and results
We consider random walks in random environment obtained as random perturbations of long–range random walks in deterministic environment. Namely, let S be a locally finite subset of Rd, d >1 and callXn the discrete time Markov chain with state spaceS that jumps from a site x to another site y with a probability p(x,y)that is proportional to ϕ(|x − y|), where ϕ:(0,∞)→(0, 1]is a positive bounded measurable function and|x|stands for the Euclidean norm ofx ∈Rd. We writeP
for the law ofXn, so that forx 6= y∈S:
P(Xn+1= y|Xn=x) =p(x,y):= ϕ(|y−x|)
wS(x) ,
where wS(x) := Pz∈S:z=6 xϕ(|z− x|). Note that the random walk Xn is well defined as soon as wS(x)∈(0,∞)for everyx ∈S. In this case,wS={wS(x), x ∈S}is a reversible measure:
wS(x)p(x,y) =wS(y)p(y,x).
Since the random walk is irreducible due to the strict positivity of ϕ, wS is the unique invariant measure up to a multiplicative constant. We shall often speak of the random walk (S,ϕ) when we need to emphasize the dependence on the state space S and the function ϕ. Typical special cases of functions ϕ will be the polynomially decaying functionϕp,α(t) :=1∧t−d−α, α > 0 and
the stretched exponential functionϕe,β(t):=exp(−tβ),β >0. We investigate here the transience
and recurrence of the random walkXn. We recall that Xn is said to berecurrentif for some x ∈S, the walk started at X0 = x returns to x infinitely many times with probability one. Because of irreducibility if this happens at some x ∈ S then it must happen at all x ∈ S. Xn is said to be transient if it is not recurrent. If we fix S = Zd, we obtain standard homogeneous lattice walks. Transience and recurrence properties of these walks can be obtained by classical harmonic analysis, as extensively discussed e.g. in Spitzer’s book [25](see also Appendix B). For instance, it is well known that for dimensiond>3 both(Zd,ϕe,β)and(Zd,ϕp,α)are transient for allβ >0 andα >0
while ford=1, 2,(Zd,ϕe,β)is recurrent for allβ >0 and(Zd,ϕp,α)is transient iff 0< α <d.
We shall be interested in the case whereSis a locally finiterandomsubset ofRd, i.e. the realization of a simple point process onRd. We denote byPthe law of the point process. For this model to be
well defined forP–almost allS we shall require that, given the choice ofϕ:
P wS(x)∈(0,∞), for all x ∈S=1 . (1.1)
If we look at the setS as a random perturbation of the regular latticeZd, the first natural question
is to find conditions on the law of the point processPand the functionϕ such that(S,ϕ)isP–a.s.
transient (recurrent) iff(Zd,ϕ)is transient (recurrent). In this case we say that the random walks (S,ϕ) and (Zd,ϕ) have a.s. the same type. A second question we shall address in this paper is that of establishing almost sure bounds on finite volume effective resistances in the case of certain recurrent random walks (S,ϕ). Before going to a description of our main results we discuss the main examples of point processes we have in mind. In what follows we shall use the notationS(Λ)
for the number of points ofS in any given bounded Borel setΛ⊂Rd. For anyt>0 andx ∈Rd we
write
Qx,t:=x+
−t
2, t 2
d
for the cube with side t and the open ball of radius t around x. To check that the models(S,ϕ)
are well defined, i.e. (1.1) is satisfied, in all the examples described below the following simple criterion will be sufficient. We write Φd, for the class of functions ϕ : (0,∞) → (0, 1] such that
R∞
0 t
d−1ϕ(t)d t<
∞. Suppose the law of the point processPis such that
sup x∈Zd
E[S(Qx,1)]<∞. (1.2)
Then it is immediate to check that(S,ϕ)satisfies (1.1) for anyϕ∈Φd.
1.1
Examples
The main example we have in mind is the case when P is ahomogeneous Poisson point process
(PPP) onRd. In this case we shall show that(S,ϕ)and(Zd,ϕ)have a.s. the same type, at least for the standard choicesϕ=ϕp,α,ϕe,β. Besides its intrinsic interest as random perturbation of lattice
walks we point out that the Poisson point process model arises naturally in statistical physics in the study of the low-temperature conductivity of disordered systems. In this context, the(S,ϕe,β)model
withβ =1 is a variant of the well known Mott variable–range hopping model, see[12]for more details. The original variable–range hopping model comes with an environment of energy marks on top of the Poisson point process that we neglect here since it does not interfere with the recurrence or transience of the walk. It will be clear that, by elementary domination arguments, all the results we state for homogeneous PPP actually apply to non–homogeneous PPP with an intensity function that is uniformly bounded from above and away from zero.
Motivated by the variable–range hopping problem one could consider point fields obtained from a crystal by dilution and spatial randomization. By crystal we mean any locally finite setΓ⊂Rd such
that, for a suitable basisv1,v2, . . . ,vd ofRd, one has
Γ−x = Γ ∀x ∈G:=z1v1+z2v2+· · ·+zdvd : zi ∈Z ∀i . (1.3)
Thespatially randomized andp–diluted crystalis obtained fromΓby first translatingΓby a random vectorV chosen with uniform distribution in the elementary cell
∆ =t1v1+t2v2+· · ·+tdvd : 06ti <1 ∀i ,
and then erasing each point with probability 1−p, independently from the others. One can check that the above construction depends only onΓand not on the particular G and∆ chosen. In the case of spatially randomized andp–diluted crystals,Pis a stationary point process, i.e. it is invariant
w.r.t. spatial translations. It is not hard to check that all the results we state for PPP hold for any of these processes as well for the associated Palm distributions (see [12] for a discussion on the Palm distribution and its relation to Mott variable–range hopping). Therefore, we shall not explic-itly mention in the sequel, to avoid lengthy repetitions, the application of our estimates to these cases. We shall also comment on applications of our results to two other classes of point processes: percolation clustersanddeterminantal point processes. We say thatS is a percolation cluster whenP
cluster has almost surely the same type as the simple random walk onZd. Our results will allow to
prove that ifS is the percolation cluster onZd then(S,ϕ)has a.s. the same type as(Zd,ϕ), at least for the standard choicesϕ=ϕp,α,ϕe,β. Determinantal point processes (DPP) on the other hand are
defined as follows, see[24; 4]for recent insightful reviews on DPP. LetK be a locally trace class self–adjoint operator on L2(Rd,d x). If, in addition,K satisfies 06K 61 we can speak of the DPP associated withK. LetP,Edenote the associated law and expectation. It is always possible to
associate withK a kernelK(x,y)such that, for any bounded measurable setB⊂Rd, one has
E[S(B)] =tr(K1B) =
Z
B
K(x,x)d x<∞ (1.4)
where S(B)is the number of points in the set B and 1B stands for multiplication by the indicator function of the setB, see[24]. Moreover, for any family of mutually disjoint subsetsD1,D2, . . . ,Dk⊂
Rd one has
E
k
Y
i=1 S(Di)
=
Z
Q
iDi
ρk(x1,x2, . . . ,xk)d x1d x2. . .d xk, (1.5)
where thek–correlation functionρksatisfies
ρk(x1,x2, . . . ,xk) =det
K(xi,xj)
16i,j6k.
Roughly speaking, these processes are characterized by a tendency towards repulsion between points, and if we consider a stationary DPP, i.e. the case where the kernel satisfies K(x,y) =
K(0,y − x), then the repulsive character forces points to be more regularly spaced than in the Poissonian case. A standard example is the sine kernel ind =1, whereK(x,y) = sin(π(x−y))
π(x−y) . Our
results will imply for instance that for stationary DPP(S,ϕ)and(Zd,ϕ)have a.s. the same type if
ϕ=ϕp,α (anyα >0) orϕ=ϕe,β withβ <d.
1.2
Random resistor networks
Our analysis of the transience and recurrence of the random walk Xn will be based on the well known resistor network representation of probabilistic quantities associated to reversible random walks on graphs, an extensive discussion of which is found e.g. in the monographs[9; 20]. For the moment let us recall a few basic ingredients of the electrical network analogy. We think of(S,ϕ)
as an undirected weighted graph with vertex setS and complete edge set {{x,y}, x 6= y}, every edge{x,y}having weight ϕ(|x− y|). The equivalent electrical network is obtained by connecting each pair of nodes{x,y}by a resistor of magnitude r(x,y):=ϕ(|x− y|)−1, i.e. by a conductance of magnitudeϕ(|x−y|). We point out that other long–range reversible random walks have already been studied (see for example [2], [3], [17], [22]and references therein), but since the resistor networks associated to these random walks are locally finite and not complete as in our case, the techniques and estimates required here are very different.
enough such that x ∈Sn, letRn(x) denote the effective resistance between the nodes x andzn in the network (S,ϕ)n. We recall that Rn(x) equals the inverse of the effective conductance Cn(x), defined as the current flowing in the network when a unit voltage is applied across the nodes x andzn. On the other hand it is well known that wS(x)Rn(x) equals the expected number of visits to x before exiting the setSn for our original random walk(S,ϕ) started at x (see, e.g., (4.28) in
[13]). The sequenceRn(x)is non–decreasing and its limitR(x)is called the effective resistance of
the resistor network(S,ϕ) between x and∞. Then, wS(x)R(x) =limn→∞wS(x)Rn(x) equals the expected number of visits to x for the walk(S,ϕ)started in x, and the walk(S,ϕ)is recurrent iff Rn(x)→ ∞for some (and therefore any) x ∈S. In the light of this, we shall investigate the rate of divergence ofRn(x) for specific recurrent models. Lower bounds onRn(x) can be obtained by the following variational characterization of the effective conductanceCn(x):
Cn(x) = inf h:S→[0,1]
h(x)=0 ,h≡1 onSnc 1 2
X
y,z∈S y6=z
ϕ(|y−z|) h(y)−h(z)2. (1.6)
The infimum above is attained when hequals the electrical potential, set to be zero on x and 1 on Snc. From (1.6) one derives Rayleigh’s monotonicity principle: the effective conductance Cn(x)
decreases whenever ϕ is replaced by ϕ′ satisfying ϕ′(t) 6 ϕ(t) for all t > 0. Upper bounds on Rn(x)can be obtained by means of fluxes. We recall that, given a point x ∈S and a subsetB ⊂S not containing x, a unit flux fromx toB is any antisymmetric function f :S×S→Rsuch that
divf(y):=X
z∈S f(y,z)
=1 if y =x,
=0 if y 6=x and y6∈B,
60 if y ∈B.
If B= ;then f is said to be a unit flux from x to∞. The energyE(f)dissipated by the flux f is defined as
E(f) =1
2
X
y,z∈S y6=z
r(y,z)f(y,z)2. (1.7)
To emphasize the dependence onS andϕwe shall often callE(f)the(S,ϕ)–energy. Finally,Rn(x), R(x)can be shown to satisfy the following variational principles:
Rn(x) =inf¦E(f) : f unit flux from x toSnc©, (1.8)
R(x) =infE(f) : f unit flux fromx to∞ . (1.9)
In particular, one has the so called Royden–Lyons criterion[19]for reversible random walks: the random walkXn is transient if and only if there exists a unit flux on the resistor network from some point x ∈S to ∞having finite energy. An immediate consequence of these facts is the following comparison tool, which we shall often use in the sequel.
Lemma 1.1. LetP,P′denote two point processes onRd such thatPis stochastically dominated byP′
1. if(S,ϕ)is transientP–a.s., then(S′,ϕ′)is transientP′–a.s.
2. if(S′,ϕ′)is recurrentP′–a.s., then(S,ϕ)is recurrentP–a.s.
Proof. The stochastic domination assumption is equivalent to the existence of a coupling ofPandP′
such that, almost surely,S⊂S′(see e.g.[14]for more details). If(S,ϕ)is transient then there exists a flux f onS with finite(S,ϕ)–energy from some x ∈S to infinity. We can lift f to a flux onS′⊃S (from the same x to infinity) by setting it equal to 0 across pairs x,y where eitherx or y (or both) are not inS. This has finite(S′,ϕ)-energy, and sinceϕ6Cϕ′it will have finite(S′,ϕ′)–energy. This proves (1). The same argument proves (2) since if S ⊂ S′ were such that(S,ϕ) is transient then
(S′,ϕ′)would be transient and we would have a contradiction.
1.3
General results
Recall the notationBx,t for the open ball inRd centered at x with radius t and define the function
ψ:(0,∞)→[0, 1]by
ψ(t):= sup x∈Zd
P S Bx,t=0. (1.10)
Theorem 1.2. (i) Let d>3andα >0, or d=1, 2and0< α <d. Suppose thatϕ∈Φd and
ϕ(t)>cϕp,α(t), (1.11)
ψ(t)6C t−γ, ∀t>0 , (1.12)
for some positive constants c,C andγ >3d+α. Then, P–a.s. (S,ϕ) is transient. (ii) Suppose that
d>3and Z
∞
0
ea tβψ(t)d t<∞, (1.13)
for some a,β > 0. Then there exists δ = δ(a,β) > 0 such that (S,ϕ) is a.s. transient whenever
ϕ(t)>c e−δtβ for some c>0. (iii) Set d>1and suppose that
sup x∈Zd
ES(Qx,1)2<∞. (1.14)
Then(S,ϕ)isP–a.s. recurrent whenever(Zd,ϕ0)is recurrent, whereϕ0 is given by
ϕ0(x,y):= max u∈Qx,1,v∈Qy,1
ϕ(|v−u|). (1.15)
The proofs of these general statements are given in Section 2. It relies on rather elementary ar-guments not far from therough embedding method described in[20, Chapter 2]. In particular, to prove (i) and (ii) we shall construct a flux on S from a point x ∈ S to infinity and show that it has finite(S,ϕ)–energy under suitable assumptions. The flux will be constructed using comparison with suitable long–range random walks onZd. Point (iii) of Theorem 1.2 is obtained by exhibiting
a candidate for the electric potential in the network(S,ϕ)that produces a vanishing conductance. Again the construction is achieved using comparison with long–range random walks onZd.
Despite the simplicity of the argument, Theorem 1.2 already captures non–trivial facts such as, e.g., the transience of the super–critical percolation cluster in dimension two withϕ=ϕp,α,α <2. More
Corollary 1.3. Fix d>1. LetPbe one of the following point processes: a homogeneous PPP; the infinite
cluster in super–critical Bernoulli site–percolation onZd; a stationary DPP on Rd. Then(S,ϕp,α)has
a.s. the same type as(Zd,ϕp,α), for allα >0.
We note that for the transience results (i) and (ii) we only need to check the sufficient conditions (1.12) and (1.13) on the function ψ(t). Remarks on how to prove bounds on ψ(t) for various processes are given in Subsection 2.2. Conditions (1.12) and (1.13) in Theorem 1.2 are in general far from being optimal. We shall give a bound that improves point (i) in the case d = 1, see Proposition 1.7 below. The limitations of Theorem 1.2 become more important whenϕ is rapidly decaying and d > 3. For instance, if P is the law of the infinite percolation cluster, then ψ(t)
satisfies a bound of the forme−c td−1, see Lemma 2.5 below. Thus in this case point (ii) would only allow to conclude that there exists a = a(p)> 0 such that, in d >3, (S,ϕ) is P–a.s. transient if
ϕ(t)>C e−a td−1. However, the well known Grimmett–Kesten–Zhang theorem about the transience ofnearest–neighborrandom walk on the infinite cluster ind>3 ([15], see also[5]for an alternative proof) together with Lemma 1.1 immediately implies that(S,ϕ) is a.s. transient for any ϕ ∈Φd. Similarly, one can use stochastic domination arguments to improve point (ii) in Theorem 1.2 for other processes. To this end we say that the process P dominates (after coarse–graining) super–
critical Bernoulli site–percolationifPis such that for some L∈Nthe random field
σ= σ(x) : x ∈Zd, σ(x):=χ S(QL x,L)>1, (1.16)
stochastically dominates the i.i.d. Bernoulli field onZd with some super–critical parameterp. Here
χ(·)stands for the indicator function of an event. In particular, it is easily checked that any homo-geneous PPP dominates super–critical Bernoulli site–percolation. For DPP defined onZd, stochastic
domination w.r.t. Bernoulli can be obtained under suitable hypotheses on the kernel K, see[21]. We are not aware of analogous conditions in the continuum that would imply that DPP dominates super–critical Bernoulli site–percolation. In the latter cases we have to content ourselves with point (ii) of Theorem 1.2 (which implies point 3 in Corollary 1.4 below). We summarize our conclusions forϕ=ϕe,β in the following
Corollary 1.4. 1. Let P be any of the processes considered in Corollary 1.3. Then, for any β > 0, (S,ϕe,β) is a.s. recurrent when d = 1, 2. 2. Let P be the law of the infinite cluster in super–critical
Bernoulli site–percolation on Zd or a homogeneous PPP or any other process that dominates super–
critical Bernoulli site–percolation. Then, for anyβ >0,(S,ϕe,β)is a.s. transient when d>3. 3. LetP
be any stationary DPP. Then, for anyβ∈(0,d),(S,ϕe,β)isP–a.s. transient when d>3
We point out that, by the same proof, statement 2) above remains true if(S,ϕe,β) is replaced by (S,ϕ),ϕ∈Φd.
1.4
Bounds on finite volume effective resistances
When a network (S,ϕ) is recurrent the effective resistances Rn(x) associated with the finite sets Sn:=S∩[−n,n]ddiverge, see (1.8), and we may be interested in obtaining quantitative information on their growth with n. We shall consider, in particular, the case of point processes in dimension d = 1, withϕ = ϕp,α, α ∈[1,∞), and the case d =2 with ϕ = ϕp,α, α ∈[2,∞). By Rayleigh’s
particular, they cover the stretched exponential case (S,ϕe,β). We say that the point process P is
dominated by an i.i.d. fieldif the following condition holds: There existsL∈Nsuch that the random field
NL= N(v) : v∈Zd, N(v):=S(QL v,L),
is stochastically dominated by independent non–negative random variables{Γv, v∈Zd}with finite
expectation. For the results in dimensiond=1 we shall require the following exponential moment condition on the dominating fieldΓ: There existsǫ >0 such that
E[eǫΓv]<∞. (1.17)
For the results in dimensiond=2 it will be sufficient to require the existence of the fourth moment:
EΓ4
v
<∞. (1.18)
It is immediate to check that any homogeneous PPP is dominated by an i.i.d. field in the sense described above and the dominating fieldΓ satisfies (1.17). Moreover, this continues to hold for non–homogeneous Poisson process with a uniformly bounded intensity function. We refer the reader
to[21; 14]for examples of determinantal processes satisfying this domination property.
Theorem 1.5. Set d =1, ϕ=ϕp,α andα>1. Suppose that the point processPis dominated by an
i.i.d. field satisfying (1.17). Then, forP–a.a. S the network (S,ϕ)satisfies: given x∈S there exists a constant c>0such that
Rn(x)>c
logn ifα=1 ,
nα−1 if1< α <2 , n/logn ifα=2, ,
n ifα >2 ,
(1.19)
for all n>2such that x∈Sn.
Theorem 1.6. Set d=2,ϕ=ϕp,α andα>2. Suppose thatPis dominated by an i.i.d. field satisfying
(1.18). Then, forP–a.a. S the network (S,ϕ) satisfies: given x ∈S there exists a constant c>0such
that
Rn(x)>c
(
logn ifα >2 ,
log(logn) ifα=2 , (1.20)
for all n>2such that x∈Sn.
The proofs of Theorem 1.5 and Theorem 1.6 are given in Section 3. The first step is to reduce the network(S,ϕ)to a simpler network by using the domination assumption. In the proof of Theorem 1.5 the effective resistance of this simpler network is then estimated using the variational principle (1.6) with suitable trial functions. In the proof of Theorem 1.6 we are going to exploit a further re-duction of the network that ultimately leads to a one–dimensional nearest–neighbor network where effective resistances are easier to estimate. This construction uses an idea that already appeared in
[18]. – see also[7]and[1]for recent applications. The construction allows to go from long–range
can be used to obtain quantitative lower bounds onRn(x)for the deterministic caseS≡Zd, which
is indeed a special case of the theorems. In the latter case the lower bounds obtained here, as well as suitable upper bounds, are probably well known but we were not able to find references to them in the literature. In appendix B, we show how to bound from above the effective resistanceRn(x)of the network(Zd,ϕp,α)by means of harmonic analysis. The resulting upper bounds match the lower
bounds of Theorems 1.5 and 1.6, with exception of the case d =1, α= 2, where our upper and lower bounds differ by a factorplogn.
1.5
Constructive proofs of transience
While the transience criteria summarized in Corollary 1.3 and Corollary 1.4 are based on known results for the deterministic networks(Zd,ϕ)obtained by classical harmonic analysis, it is possible to give constructive proofs of these results by exhibiting explicit fluxes with finite energy on the network under consideration. We discuss two results here in this direction. The first gives an improvement over the criterion in Theorem 1.2, part (i), in the case d = 1. This can be used, in particular, to give a “flux–proof” of the well known fact that(Z,ϕp,α) is transient forα <1. The second result gives a constructive proof of transience of a deterministic network, which, in turn, reasoning as in the proof of Theorem 1.2 part (i), gives a flux–proof that(Z2,ϕp,α)is transient for
α <2. In order to state the one-dimensional result, it is convenient to number the points ofS as S={xi}i∈I where xi < xi+1, x−1 <06x0 andN⊂ I or−N⊂I (we assume that|S|=∞,P–a.s.,
since otherwise the network is recurrent). For simplicity of notation we assume below thatN⊂I,
P–a.s. The following result can be easily extended to the general case by considering separately
the conditional probabilitiesP(·|N⊂ I)andP(·|N6⊂ I), and applying a symmetry argument in the
second case.
Proposition 1.7. Take d = 1 and α ∈ (0, 1). Suppose that the following holds for some positive constants c,C:
ϕ(t)>cϕp,α(t), t>0 , (1.21)
E |xn−xk|1+α6C(n−k)1+α, ∀n>k>0 . (1.22)
ThenP–a.s.(S,ϕ)is transient. In particular, ifPis a renewal point process such that
E(|x1−x0|1+α)<∞, (1.23)
thenP–a.s.(S,ϕ)is transient.
Suppose thatPis a renewal point process and write
e
ψ(t):=P(x1−x0>t).
Then (1.23) certainly holds as soon as e.g.ψesatisfiesψe(t)6C t−(1+α+ǫ)for some positive constants C,ǫ. We can check that this improves substantially over the requirement in Theorem 1.2, part (i), since ifψis defined by (1.10), then we have, for allt>1:
˜
ψ(2t) =P(S∩Bx0+t,t=;)6ψ(t−1).
IdentifyR2 with the complex planeC, and define the setS
∗:=∪∞n=0Cn, where
Cn:=nnekn2+iπ1 ∈C: k∈ {0, . . . ,n}
o
. (1.24)
Theorem 1.8. The network(S∗,ϕp,α)is transient for allα∈(0, 2).
This theorem, together with the comparison techniques developed in the next section (see Lemma 2.1 below), allows to recover by a flux–proof the transience of(Z2,ϕp,α)forα∈(0, 2). The proofs
of Proposition 1.7 and Theorem 1.8 are given in Section 4.1.
2
Recurrence and transience by comparison methods
LetS0 be a given locally finite subset of Rd and let(S0,ϕ0)be a random walk on S0. We assume thatwS0(x)<∞for all x ∈S0 and thatϕ0(t)>0 for allt >0. Recall that in the resistor network picture every node {x,y} is given the resistance r0(x,y) := ϕ0(|x − y|)−1. To fix ideas we may think ofS0=Zd and eitherϕ0 =ϕp,α orϕ0=ϕe,β. (S0,ϕ0)will play the role of the deterministic
background network.
LetPdenote a simple point process onRd, i.e. a probability measure on the setΩ of locally finite subsetsS ofRd, endowed with theσ–algebraF generated by the counting mapsNΛ:Ω→N∪ {0},
whereNΛ(S) =S(Λ)is the number of points ofS that belong toΛandΛis a bounded Borel subset ofRd. We shall useSto denote a generic random configuration of points distributed according toP.
We assume thatPandϕ are such that (1.1) holds. Next, we introduce a mapφ:S0→S, from our
reference setS0to the random setS. For anyx ∈S0we writeφ(x) =φ(S,x)for the closest point in Saccording to Euclidean distance. If the Euclidean distance from x toS is minimized by more than one point inS then chooseφ(x)to be the lowest of these points according to lexicographic order. This defines a measurable mapΩ∋S7→φ(S,x)∈Rd for every x ∈S0. For any pointu∈S define
thecell
Vu:={x ∈S0 : u=φ(x)}.
By construction{Vu, u∈S} determines a partition of the original vertex set S0. Clearly, some of theVu may be empty, while some may be large (ifS has large “holes” with respect toS0). Let N(u) denote the number of points (ofS0) in the cellVu. We denote byEthe expectation with respect to P.
Lemma 2.1. Suppose(S0,ϕ0)is transient. If there exists C <∞such that, for all x6= y in S0,
EN(φ(x))N(φ(y))r(φ(x),φ(y)) 6C r0(x,y), (2.1)
then(S,ϕ)isP–a.s. transient.
Proof. Without loss of generality we shall assume that 0 ∈ S0. Since (S0,ϕ0) is transient, from the Royden–Lyons criterion recalled in Subsection 1.2, we know that there exists a unit flux f : S0×S0→Rfrom 0∈S0 to∞with finite(S0,ϕ0)-energy. By the same criterion, in order to prove the transience of(S,ϕ) we only need to exhibit a unit flux from some point of S to∞with finite
(S,ϕ)-energy. To this end, for anyu,v∈S we define
θ(u,v) = X
x∈Vu
X
y∈Vv
If eitherVu or Vv are empty we setθ(u,v) =0. Note that the above sum is finite for all u,v ∈S,
P–a.s. Indeed condition (2.1) implies thatN(φ(x))<∞for all x∈S0,P–a.s. Thus,θdefines a unit flux fromφ(0)to infinity on(S,ϕ). Indeed, for everyu,v ∈S we haveθ(u,v) =−θ(v,u)and for everyu6=φ(0)we havePv∈Sθ(u,v) =0. Moreover,
X
v∈S
θ(φ(0),v) = X
x∈Vφ(0) X
y∈S0
f(x,y) = X
x∈Vφ(0):
x6=0
X
y∈S0
f(x,y) + X
y∈S0
f(0,y) =0+1=1 .
The energy of the fluxθ is given by
E(θ):= 1
2
X
u∈S
X
v∈S
θ(u,v)2r(u,v). (2.2)
From Schwarz’ inequality
θ(u,v)26N(u)N(v)X
x∈Vu
X
y∈Vv
f(x,y)2.
It follows that
E(θ)61
2
X
x∈S0
X
y∈S0
f(x,y)2N(φ(x))N(φ(y))r(φ(x),φ(y)). (2.3)
Sincef has finite energy on(S0,ϕ0)we see that condition (2.1) impliesE[E(θ)]<∞. In particular, this shows that P–a.s. there exists a unit fluxθ from some point u0 ∈S to∞ with finite (S,ϕ)
-energy. To produce an analogue of Lemma 2.1 in the recurrent case we introduce the
seteS=S∪S0 and consider the network(eS,ϕ). From monotonicity of resistor networks, recurrence of(S,ϕ)is implied by recurrence of(eS,ϕ). We define the mapφ′:eS→S0, fromSeto the reference setS0 as the mapφ introduced before, only with S0 replaced bySeandS replaced byS0. Namely, given x ∈Sewe defineφ′(x)as the closest point inS0 according to Euclidean distance (when there is more than one minimizing point, we take the lowest of these points according to lexicographic order). Similarly, for any pointx ∈S0we define
Vx′:={u∈eS : x=φ′(u)}.
Thus {Vx′, x ∈S0} determines a partition of Se. Note that in this case all Vx′ are non–empty (Vx′ containsx ∈Se). As an example, ifS0=Zd andx ∈eS, thenφ′(x)is the only point y inZd such that x ∈y+ (−1/2, 1/2]d, whileVx′=eS∩(x+ (−12,12]d)for anyx ∈Zd.
Lemma 2.2. Suppose that(S0,ϕ0)is recurrent and thatP–a.s. Vx′is finite for all x∈S0. If there exists C <∞such that, for all x6= y in S0,
Eh X
u∈V′
x
X
v∈V′
y
ϕ(|u−v|)i6Cϕ0(|x− y|), (2.4)
then(S,ϕ)isP–a.s. recurrent.
zn, i.e. the net current flowing in the network when a unit voltage is applied across 0 andzn. Since
(S0,ϕ0)is recurrent we know thatc(S0,n)→0,n→ ∞. Recall thatc(S0,n)satisfies
c(S0,n) = 1
2
X
x,y∈S0
ϕ0(|x−y|)(ψn(x)−ψn(y))2, (2.5)
whereψnis the electric potential, i.e. the unique function on S0 that is harmonic inS0,n, takes the value 1 at 0 and vanishes outside ofS0,n. GivenS∈Ω, set
e
Sn=∪x∈S0,nVx′.
Note thatSen is an increasing sequence of finite sets, covering all S. Collapse all sites in(eSn)c into a single siteezn and callc(Sen)the effective conductance between 0 andezn (by construction 0∈Sen). From the Dirichlet principle (1.6) we have
c(Sen)6 1 2
X
u,v∈eS
ϕ(|u−v|)(g(u)−g(v))2,
for any g:eS→[0, 1]such thatg(0) =1 andg=0 on(Sen)c. Choosingg(u) =ψ
n(φ′(u))we obtain
c(Sen)6 1 2
X
x,y∈S0
(ψn(x)−ψn(y))2
X
u∈V′
x
X
v∈V′
y
ϕ(|u−v|).
From the assumption (2.4) and the recurrence of (S0,ϕ0) implying that (2.5) goes to zero, we deduce thatE[c(Sen)]→0,n→ ∞. Since c(Sen)is monotone decreasing we deduce thatc(Sen)→0,
P–a.s. This implies theP–a.s. recurrence of(Se,ϕ)and the claim follows.
2.1
Proof of Theorem 1.2
We first prove part (i) of the theorem, by applying the general statement derived in Lemma 2.1 in the case S0 = Zd andϕ0 = ϕp,α. Since (S0,ϕp,α) is transient whenever d > 3, or d = 1, 2 and 0< α <d (the classical proof of these facts through harmonic analysis is recalled in Appendix B), we only need to verify condition (2.1). For the moment we only suppose that ψ(t) 6 C′t−γ for someγ > 0. Let us fix p,q >1 s.t. 1/p+1/q =1. Using Hölder’s inequality and then Schwarz’ inequality (or simply using Hölder inequality with the triple(1/2q, 1/2q, 1/p)), we obtain
EN(φ(x))N(φ(y))r(φ(x),φ(y)) (2.6)
6EN(φ(x))2q 1
2qEN(φ(y))2q 1
2qEr(φ(x),φ(y))p 1 p
for anyx 6= y inZd. By assumption (1.11) we know that
r(φ(x),φ(y))p6c rp,α(φ(x),φ(y))p:=c 1∨ |φ(x)−φ(y)|p(d+α)
. (2.7)
We shall usec1,c2, . . . to denote constants independent ofx and y below. From Jensen’s inequality
|φ(x)−φ(y)|p(d+α)6c1
From (2.7) and the fact that|x−y|>1 we derive that
Er(φ(x),φ(y))p 6c2sup z∈Zd
E|φ(z)−z|p(d+α)+c2|x−y|p(d+α). (2.8)
Now we observe that|φ(z)−z|>t if and only ifBz,t∩S=;. Hence we can estimate
E|φ(z)−z|p(d+α) 61+
Z ∞
1
ψ
t
1 p(d+α)
d t61+C
Z ∞
1 t−
γ
p(d+α)d t6c
3,
provided thatγ >p(d+α). Therefore, using|x− y|>1, from (2.8) we see that for any x 6= y in
Zd:
Er(φ(x),φ(y))p 1 p 6c
4rp,α(x,y). (2.9)
Next, we estimate the expectationEN(φ(x))2qfrom above, uniformly in x∈Zd. To this end we
shall need the following simple geometric lemma.
Lemma 2.3. Let E(x,t) be the event that S∩B(x,t) 6= ; and S∩B(x ±3pd t ei,t) 6= ;, where
{ei : 16i6d}is the canonical basis ofRd. Then, on the event E(x,t)we haveφ(x)∈B(x,t), i.e.
|φ(x)−x|<t, and z6∈Vφ(x)for all z∈Rd such that|z−x|>9dpd t
Assuming for a moment the validity of Lemma 2.3 the proof continues as follows. From Lemma 2.3 we see that, for a suitable constant c5, the event N(φ(x)) > c5td implies that at least one of the 2d+1 ballsB(x,t),B(x±3pd t ei,t)must have empty intersection withS. SinceB(x±⌊3
p
d t⌋ei,t− 1)⊂B(x±3pd t ei,t)fort>1, we conclude that
PN(φ(x))>c5td
6(2d+1)ψ(t−1), t>1.
Takingc6 such thatc
−1d
5 c
1 2qd
6 =2, it follows that
EN(φ(x))2q=
Z ∞
0
P(N(φ(x))2q>t)d t
6c6+ (2d+1)
Z ∞
c6
ψ
c−
1 d
5 t
1 2qd −1
d t6c6+c7
Z ∞
1 t−
γ
2qdd t6c
8, (2.10)
as soon asγ >2qd. Due to (2.6), (2.9) and (2.10), the hypothesis (2.1) of Lemma 2.1 is satisfied
whenψ(t)6C t−γfor all t>1, whereγis a constant satisfying
γ >p(d+α), γ >2qd= 2pd
p−1.
We observe that the functions (1,∞) ∋ p 7→ p(d +α) and (1,∞) ∋ p 7→ 2pd
p−1 are respectively increasing and decreasing and intersect in only one pointp∗=3d+α
d+α. Hence, optimizing overp, it is
enough to require that
γ > inf
p>1max{p(d+α), 2pd
This concludes the proof of Theorem 1.2 (i). Proof of lemma 2.3. The first claim is trivial since
S∩B(x,t)6= ; implies φ(x) ∈B(x,t). In order to prove the second one we proceed as follows. For simplicity of notation we setm:=3pd andk:=9d. Let us takez∈Rd with|z−x|>kpd t.
Without loss of generality, we can suppose that x =0, z1 >0 and z1 >|zi| for all i =2, 3, . . . ,d. Note that this implies thatkpd t<|z|6pdz1, hencez1>kt. Since
min
u∈B(0,t)|z−u|=|z| −t
max u∈B(mt e1,t)
|z−u|6|z−mt e1|+t,
if we prove that
|z| − |z−mt e1|>2t, (2.11) we are sure that the distance fromz to each point inS∩B(0,t)is larger than the distance fromz to each point ofS∩B(mt e1,t). Hence it cannot be thatz∈Vφ(0). In order to prove (2.11), we first observe that the map(0,∞)∋ x 7→px+a−px+b∈(0,∞)is decreasing fora> b. Hence we obtain that
|z| − |z−mt e1|>
p
dz1−Æ(z1−mt)2+ (d−1)z2 1. Therefore, settingx :=z1/t, we only need to prove that
p
d x−p(x−m)2+ (d−1)x2>2 , ∀x >k.
By the mean value theorem applied to the function f(x) =px
p
d x−p(x−m)2+ (d−1)x2> 1 2pd x
d x2−(x−m)2−(d−1)x2=
2x m−m2
2pd x > m
p
d − m2
k =2 .
This completes the proof of (2.11).
Proof of Theorem 1.2 Part (ii). We use the same approach as in Part (i) above. We start again our estimate from (2.6). Moreover, as in the proof of (2.10) it is clear that hypothesis (1.13) implies
E[N(φ(x))2q]<∞for anyq>1, uniformly in x∈Zd. Therefore it remains to check that
r0(x,y):=Er(φ(x),φ(y))p 1
p , (2.12)
defines a transient resistor network onZd, for anyd>3, under the assumption that
r(φ(x),φ(y))6C eδ|φ(x)−φ(y)|β.
For anyβ >0 we can find a constantc1=c1(β)such that
r(φ(x),φ(y))6C expδc1|φ(x)−x|β+|x−y|β+|φ(y)− y|β.
Therefore, using Schwarz’ inequality we have
Er(φ(x),φ(y))p1p
6c2expδc2|x−y|βEexpδc2|φ(x)−x|β 1
2Eexpδc
2|φ(y)−y|β
Forγ >0
Eexpγ|φ(x)−x|β61+
Z ∞
1
ψ 1
γ logt
1
β
!
d t
=1+γβ
Z ∞
0
ψ(s)eγsβsβ−1ds,
where, using (1.13), the last integral is finite forγ <a. Takingγ=δc2 andδsufficiently smal we arrive at the conclusion that uniformly inx,
Er(φ(x),φ(y))p 1 p 6c
3exp
c3|x−y|β=:er0(x,y).
Clearly,er0(x,y)defines a transient resistor network onZd and the same claim for r0(x,y)follows
from monotonicity. This ends the proof of Part (ii) of Theorem 1.2.
Proof of Theorem 1.2 Part (iii). Here we use the criterion given in Lemma 2.2 with S0=Zd. With this choice ofS0 we have thatVx′⊂ {x} ∪(S∩Qx,1), x∈Zd. Recalling definition (1.15) we see that for allx 6= y inZd,
E
X
u∈V′
x
X
v∈V′
y
ϕ(|u−v|)
6c1ϕ0(x,y)E(1+S(Qx,1))(1+S(Qy,1)).
Using Schwarz’ inequality and condition (1.14) the last expression is bounded above byc2ϕ0(x,y). This implies condition (2.4) and therefore the a.s. recurrence of(S,ϕ).
2.2
Proof of Corollary 1.3
We start with some estimates on the functionψ(t). Observe that, for Poisson point processes PPP(λ), one hasψ(t) = e−λtd. A similar estimate holds for a stationary DPP. More generally, for DPP we shall use the following facts.
Lemma 2.4. Let P be a determinantal point process on Rd with kernel K. Then the function ψ(t)
defined in (1.10) equals
ψ(t) = sup x∈Zd
Y
i
1−λi(B(x,t))
, (2.13)
whereλi(B)denote the eigenvalues ofK1Bfor any bounded Borel set B⊂Rd. In particular, condition (1.12) is satisfied if
exp
n
−
Z
B(x,t)
K(u,u)du
o
6C t−γ, t>0 , x ∈Zd, (2.14)
for some constants C>0andγ >3d+α. IfPis a stationary DPP thenψ(t)6e−δtd, t>0, for some
δ >0. Finally, condition (1.14) reads
sup x∈Zd
nX
i
λi(Q(x, 1)) +
X
i
X
j:j6=i
λi(Q(x, 1))λj(Q(x, 1))
o
<∞. (2.15)
Proof. It is known (see [4] and [24]) that, for each bounded Borel set B ⊂ Rd, the number of
pointsS(B)has the same law as the sumPiBi,Bi’s being independent Bernoulli random variables with parameters λi(B). This implies identity (2.13). Since 1−x 6 e−x, x > 0, we can bound the r.h.s. of (1.10) byQie−λi(B) = e−T r(K1B). This identity and (1.4) imply (2.14). If the DPP is
stationary thenK(u,u)≡K(0)>0 and thereforeψ(t)6e−K(0)td. Finally, (2.15) follows from the identityE[S(Q(x, 1))2] =Piλi(Q(x, 1)) +
P
i
P
j:j6=iλi(Q(x, 1))λj(Q(x, 1)), again a consequence of the fact that S(Q(x, 1)) is the sum of independent Bernoulli random variables with parameters
λi(Q(x, 1)). Since the sum of theλi(Q(x, 1))’s is finite,E[S(Q(x, 1))2]<∞for any x ∈Zd. If the DPP is stationary it is uniformly finite. The next lemma allows to estimateψ(t)in the case of
percolation clusters.
Lemma 2.5. LetPbe the law of the infinite cluster in super–critical Bernoulli site (or bond) percolation
inZd, d>2. Then there exist constants k,δ >0such that
e−δ−1nd−16ψ(n)6k e−δnd−1, n∈N.
Proof. The lower bound follows easily by considering the event that e.g. the cube centered at the origin with sidenhas all the boundary sites (or bonds) unoccupied. To prove the upper bound one can proceed as follows. LetKn(γ),γ >0, denote the event that there exists an open clusterC inside the box B(n) = [−n,n]d∩Zd such that|C|>γnd. Known estimates (see e.g. Lemma (11.22) in Grimmett’s book[16]for the case d =2 and Theorem 1.2 of Pisztora’s[23]ford >3) imply that there exist constantsk1,δ1,γ >0 such that
P(Kn(γ)c)6k1e−δ1nd−1. (2.16)
On the other hand, let Cx denote the open cluster at x ∈Zd and writeC∞ for the infinite open
cluster. From [16, Theorem (8.65)] we have that there exist constants k2,δ2 such that for any x ∈Zd and for anyγ >0:
P(γnd6|Cx|<∞)6k2e−δ2nd−1. (2.17)
Now we can combine (2.16) and (2.17) to prove the desired estimate. For anynwe write
P(B(n)∩ C∞=;)6P(B(n)∩ C∞=;; Kn(γ)) +P(Kn(γ)c).
The last term in this expression is bounded using (2.16). The first term is bounded by
P(∃x ∈B(n) : γnd 6|Cx|<∞)6 X
x∈B(n)
P(γnd 6|Cx|<∞).
Using (2.17) we arrive atP(B(n)∩ C∞=;)6k e−δnd−1for suitable constantsk,δ >0. We are now ready to finish the proof of Corollary 1.3. It is clear from the previous lemmas that in all cases we have both conditions (1.12) and (1.14). Moreover it is easily verified that(Zd,ϕ0)and (Zd,ϕp,α)have the same type, whenϕ0is defined by (1.15) with ϕ=ϕp,α. This ends the proof of
2.3
Proof of Corollary 1.4
It is easily verified that(Zd,ϕ0) and(Zd,ϕe,β)have the same type, whenϕ0 is defined by (1.15)
withϕ=ϕe,β. Therefore the statement about recurrence follows immediately from Theorem 1.2,
Part (iii) and the fact that in all cases (1.14) is satisfied (see the previous Subsection).
To prove the second statement we recall that our domination assumption and Strassen’s theo-rem imply that on a suitable probability space (Ω,P) one can define the random field(σ1,σ2) ∈
{0, 1}Zd× {0, 1}Zd such thatσ
1 has the same law as the infinite cluster in a super–critical Bernoulli site–percolation onZd,σ2 has the same law as the random fieldσdefined in (1.16) andσ16σ2,
P–a.s., i.e. σ1(x)6σ2(x)for allx ∈Zd,P–a.s. With eachσ1 we associate the nearest–neighbor resistor networkN1 with nodes {x ∈Zd : σ1(x) =1}such that nearest–neighbor nodes are con-nected by a conductance of valuec0=c0(L)>0 to be determined below. We know from[15]that
N1 is transient a.s. Now, for each cubeQL x,LintersectingS we fix a point ¯x∈QL x,L∩S(say the low-est one according to lexicographic order). If we keep all points ¯x’s belonging to the infinite cluster inσ2 and neglect all other points ofS we obtain a subnetwork (Se,ϕ)of(S,ϕ). Ifc0 is sufficiently small we haveϕ(y,z)>c0for all y,z∈Ssuch that y∈QL x
1,L,z∈QL x2,L,x1,x2∈Z
d,
|x1−x2|=1. Reasoning as in the proof of Lemma 1.1 then immediately implies the a.s. transience of(S,ϕ). Note that this actually works for anyϕ∈Φd.
To prove the third statement we observe that, for stationary DPP, ψ(t) 6 e−δtd, see Lemma 2.4. Therefore the claim follows from Theorem 1.2, Part (ii).
3
Lower bounds on the effective resistance
Assume that P is dominated by an i.i.d. field Γ as stated before Theorem 1.5 and suppose the domination property holds with some fixed L∈N. We shall writeQv for the cubeQv L,L. To prove
Theorem 1.5 and Theorem 1.6 we only need to show that, given v0∈Zd, forP–a.a.S there exists
a positive constantc such that, for all x ∈S∩Qv0L,L, the lower bounds (1.19) and (1.20) onRn(x)
hold. We restrict tov0=0, since the general case can be treated similarly.
We start by making a first reduction of the network, which uses the stochastic domination assump-tion. This procedure works in any dimension d. First, we note that it is sufficient to prove the bounds in the two theorems for the quantity ˆRn(x), defined as the effective resistance from x to Qc0,2Ln, instead ofRn(x), which is the effective resistance from x toQc0,2n. In particular, there is no loss of generality in taking L = 1, in which case ˆRn(x) =Rn(x). The next observation is that, by monotonicity,Rn(x)is larger than the same quantity computed in the network obtained by collaps-ing into a scollaps-ingle node vall points in each cubeQv, v∈Zd. We now have a network with nodes on the points ofZd (although some of them may be empty). Note that across two nodesu,v we have
NuNv wires each with a resistance bounded from below by
ρu,v:=c|u−v|d+α
for a suitable (non–random) constantc>0. Moreover, using the stochastic domination assumption we know thatNu 6Γu for allu∈Zd, and we can further lower the resistance by considering the
the results will follow once we prove that, for(Γ,ρ), the effective resistance from 0 toQc0,2n={u∈
Zd : kuk∞>n}satisfies the desired bounds. From now on we consider the cases d=1 andd =2 separately.
3.1
Proof of Theorem 1.5
Setd=1. We further reduce the network(Γ,ρ)introduced above by collapsing into a single node
eveach pair{v,−v}. This gives a network on{0, 1, 2, . . .}where across each pair 06i< j there are noweΓieΓj wires, where eΓi :=
