The free law of the field is specified by the density exp

FRANCESCO CARAVENNA AND JEAN–DOMINIQUE DEUSCHEL

Abstract. We consider a random field ϕ : {1, . . . , N } → R as a model for a linear chain attracted to the defect line ϕ = 0, i.e. the x–axis. The free law of the field is specified by the density exp` − P_iV(∆ϕi)´ with respect to the Lebesgue measure on R^N, where

∆ is the discrete Laplacian and we allow for a very large class of potentials V (·). The interaction with the defect line is introduced by giving the field a reward ε ≥ 0 each time it touches the x–axis. We call this model the pinning model. We consider a second model, the wetting model, in which, in addition to the pinning reward, the field is also constrained to stay non-negative.

We show that both models undergo a phase transition as the intensity ε of the pinning reward varies: both in the pinning (a = p) and in the wetting (a = w) case, there exists a critical value ε^ac such that when ε > ε^ac the field touches the defect line a positive fraction of times (localization), while this does not happen for ε < ε^a_c (delocalization).

The two critical values are non-trivial and distinct: 0 < ε^p_c < ε^w_c < ∞, and they are the only non-analyticity points of the respective free energies. For the pinning model the transition is of second order, hence the field at ε = ε^p_c is delocalized. On the other hand, the transition in the wetting model is of first order and for ε = ε^w_c the field is localized.

The core of our approach is a Markov renewal theory description of the field.

1. Introduction and main results

1.1. Definition of the models. We are going to define two distinct but related models for a (1+1)–dimensional random field. These models depend on a measurable function V (·) : R → R ∪ {+∞}, the potential. We require that x 7→ exp(−V (x)) is bounded and continuous and that R

Rexp(−V (x)) dx < ∞. Since a global shift on V (·) is irrelevant for our purposes, we will actually impose the stronger condition

Z

R

e^{−V (x)}dx = 1 . (1.1)

The last assumptions we make on V (·) are that V (0) < ∞, i.e. exp(−V (0)) > 0, and that Z

R

x²e^{−V (x)}dx =: σ² < ∞ and Z

R

x e^{−V (x)}dx = 0 . (1.2) The most typical example is of course V (x) ∝ x², but we stress that we do not make any convexity assumption on V (·). Next we introduce the Hamiltonian H[a,b](ϕ), defined for a, b ∈ Z, with b − a ≥ 2, and for ϕ : {a, . . . , b} → R by

H[a,b](ϕ) :=

Xb−1 n=a+1

V ∆ϕ_n

, (1.3)

Date: March 14, 2007.

2000 Mathematics Subject Classification. 60K35, 60F05, 82B41.

Key words and phrases. Pinning Model, Wetting Model, Phase Transition, Entropic Repulsion, Markov Renewal Theory, Local Limit Theorem, Perron–Frobenius Theorem, FKG Inequality.

1

where ∆ denotes the discrete Laplacian:

∆ϕ_n := (ϕ_n+1− ϕn) − (ϕn− ϕn−1) = ϕ_n+1+ ϕ_n−1− 2ϕn. (1.4) We are ready to introduce our first model, the pinning model (p-model for short) P^p_ε,N, that is the probability measure on R^{N −1} defined by

P^p

ε,N dϕ₁· · · dϕN −1

:= exp − H[−1,N+1](ϕ)
Zε,N^p

N −1Y

i=1

ε δ₀(dϕ_i) + dϕ_i

(1.5)

where N ∈ N, ε ≥ 0, dϕⁱ is the Lebesgue measure on R, δ0(·) is the Dirac mass at zero and Zε,N^p is the normalization constant, usually called partition function. To complete the definition, in order to make sense of H_[−1,N+1](ϕ), we have to specify:

the boundary conditions ϕ₋₁ = ϕ0 = ϕN = ϕN +1:= 0 . (1.6) We fix zero boundary conditions for simplicity, but our approach works for arbitrary choices (as long as they are bounded in N ).

The second model we consider, the wetting model (w-model for short) P^w_ε,N, is a variant of the pinning model defined by

P^w_ε,N dϕ₁· · · dϕN −1

:= P^p_ε,N dϕ₁· · · dϕN −1

 ϕ₁≥ 0, . . . , ϕN −1≥ 0

= exp − H_[−1,N+1](ϕ)
Z_ε,N^w

N −1Y

i=1

ε δ₀(dϕ_i) + dϕ_i1_(ϕi≥0)

, (1.7)

i.e. we replace the measure dϕ_i by dϕ_i1_(ϕi≥0) and Zε,N^p by a new normalization Zε,N^w . Both P^p_ε,N and P^w_ε,N are (1+1)–dimensional models for a linear chain of length N which is attracted to a defect line, the x–axis, and the parameter ε ≥ 0 tunes the strength of the attraction. By ‘(1+1)–dimensional’ we mean that the configurations of the linear chain are described by the trajectories {(i, ϕi)}0≤i≤N of the field, so that we are dealing with directed models (see Figure 1 for a graphical representation). We point out that linear chain models with Laplacian interaction appear naturally in the physical literature in the context of semiflexible polymers, cf. [6, 21] (however the scaling they consider is different from the one we look at in this paper). An interesting interpretation of P^w_ε,N as a model for the DNA denaturation transition will be discussed below. One note about the terminology: while ‘pinning’ refers of course to the attraction terms ε δ0(dϕi), the use of the term ‘wetting’ is somewhat customary in the presence of a positivity constraint and refers to the interpretation of the field as an effective model for the interface of separation between a liquid above a wall and a gas, cf. [13].

The purpose of this paper is to investigate the behavior of P^p_ε,N and P^w_ε,N in the large N limit: in particular we wish to understand whether and when the reward ε ≥ 0 is strong enough to pin the chain at the defect line, a phenomenon that we will call localization. We point out that this kind of questions have been answered in depth in the case of gradient interaction, i.e. when the Laplacian ∆ appearing in (1.3) is replaced by the discrete gradient

∇ϕⁿ:= ϕn− ϕn−1, cf. [17, 15, 18, 13, 11, 1]: we will refer to this as the gradient case. As we are going to see, the behavior in the Laplacian case turns out to be sensibly different.

0000000000000000000000000000000000 0000000000000000000000000000000000 0000000000000000000000000000000000 1111111111111111111111111111111111 1111111111111111111111111111111111 1111111111111111111111111111111111

N

N N + 1

N + 1

−1

−1 0

0 {ϕn}n

{ϕn}n

pinning model P^p_ε,N

wetting model P^w_ε,N

Figure 1. A graphical representation of the pinning model P^p_ε,N (top) and of the wetting model P^w_ε,N (bottom), for N = 25 and ε > 0. The trajectories {(n, ϕn)}0≤n≤N of the field describe the configurations of a linear chainattracted to a defect line, the x–axis. The grey circles represent the pinned sites, i.e. the points in which the chain touches the defect line, which are energetically favored. Note that in the pinning case the chain can cross the defect line without touching it, while this does not happen in the wetting case due to the presence of a wall, i.e. of a constraint for the chain to stay non-negative: the repulsion effect of entropic nature that arises is responsible for the different critical behavior of the models.

1.2. The free energy and the main results. A convenient way to define localization for our models is by looking at the Laplace asymptotic behavior of the partition function Zε,N^a as N → ∞. More precisely, for a ∈ {p, w} we define the free energy f^a(ε) by

f^a(ε) := lim

N →∞ f^a_N(ε) , f^a_N(ε) := 1

N log Zε,N^a , (1.8) where the existence of this limit (that will follow as a by-product of our approach) can be proven with a standard super-additivity argument. The basic observation is that the free energy is non-negative. In fact, setting Ω^p:= [0, ∞) and Ω^w:= R, we have ∀N ∈ N

Zε,N^a = Z

exp − H_[−1,N+1](ϕ)
^{N −1}Y

i=1

ε δ₀(dϕ_i) + dϕ_i1_(ϕi∈Ω^a₎

≥ Z

exp − H_[−1,N+1](ϕ)
^{N −1}Y

i=1

dϕ_i1_(ϕi∈Ω^a₎ = Z0,N^a ≥ c₁ N^c2 ,

(1.9)

where c1, c2 are positive constants and the polynomial bound for Z0,N^a (analogous to what happens in the gradient case, cf. [13]) is proven in (2.14). Therefore f^a(ε) ≥ f^a(0) = 0 for every ε ≥ 0. Since this lower bound has been obtained by ignoring the contribution of the paths that touch the defect line, one is led to the following

Definition 1.1. For a ∈ {p, w}, the a-model {P^aε,N}N is said to be localized if f^a(ε) > 0.

The first problem is to understand for which values of ε (if any) there is localization.

Some considerations can be drawn easily. We introduce for convenience for t ∈ R

ef^a_N(t) := f^a_N(e^t) ef^a(t) := f^a(e^t) . (1.10) It is easy to show (see Appendix A) that ef^a_N(·) is convex, therefore also ef^a(·) is convex. In particular, the free energy f^a(ε) =ef^a(log ε) is a continuous function, as long as it is finite.

f^a(·) is also non-decreasing, because Zε,N^a is increasing in ε (cf. the first line of (1.9)). This observation implies that, for both a ∈ {p, w}, there is a critical value ε^ac ∈ [0, ∞] such that the a-model is localized if and only if ε > ε^a_c. Moreover ε^p_c ≤ ε^wc , since Z_ε,N^p ≥ Zε,N^w .

However it is still not clear that a phase transition really exists, i.e. that ε^a_c ∈ (0, ∞).

Indeed, in the gradient case the transition is non-trivial only for the wetting model, i.e.

0 < ε^w,∇c < ∞ while ε^p,∇c = 0, cf. [13, 17]. Our first theorem shows that in the Laplacian case both the pinning and the wetting model undergo a non-trivial transition, and gives further properties of the free energy f^a(·).

Theorem 1.2 (Localization transition). The following relations hold:

ε^p_c ∈ (0, ∞) ε^w_c ∈ (0, ∞) ε^p_c < ε^w_c .

We have f^a(ε) = 0 for ε ∈ [0, ε^ac], while 0 < f^a(ε) < ∞ for ε ∈ (ε^ac, ∞), and as ε → ∞ f^a(ε) = log ε (1 + o(1)) a ∈ {p, w} . (1.11) Moreover the function f^a(ε) is real analytic on (ε^a_c, ∞).

One may ask why in the Laplacian case we have ε^p_c > 0, unlike in the gradient case.

Heuristically, we could say that the Laplacian interaction (1.3) describes a stiffer chain, more rigid to bending with respect to the gradient interaction, and therefore Laplacian models require a stronger reward in order to localize. Note in fact that in the Gaussian case V (x) ∝ x² the ground state of the gradient interaction is just the horizontally flat line, whereas the Laplacian interaction favors rather affine configurations, penalizing curvature and bendings.

It is worth stressing that the free energy has a direct translation in terms of some path properties of the field. Defining the contact number ℓ_N by

ℓ_N := #

i ∈ {1, . . . , N} : ϕi = 0

, (1.12)

a simple computation (see Appendix A) shows that for every ε > 0 and N ∈ N d^a_N(ε) := E^a_ε,N

ℓ_N N



= (ef^a_N)^′(log ε) = ε · (f^aN)^′(ε) . (1.13) Then, introducing the non-random quantity d^a(ε) := ε · (f^a)^′(ε) (which is well-defined for ε 6= ε^ac by Theorem 1.2), a simple convexity argument shows that d^a_N(ε) → d^a(ε) as N → ∞, for every ε 6= ε^ac. Indeed much more can be said (see Appendix A):

• When ε > ε^ac we have that d^a(ε) > 0, and for every δ > 0 and N ∈ N P^a_ε,N 

 ℓ_N

N − d^a(ε)   > δ



≤ exp(−c3N ) , (1.14)

where c₃ is a positive constants. This shows that, when the a-model is localized according to Definition 1.1, its typical paths touch the defect line a positive fraction of times, equal to d^a(ε). Notice that, by (1.11) and convexity arguments, d^a(ε) converges to 1 as ε → ∞, i.e. a strong reward pins the field at the defect line in a very effective way (observe that ℓN/N ≤ 1).

• On the other hand, when ε < ε^ac we have d^a(ε) = 0 and for every δ > 0 and N ∈ N P^a_ε,N

ℓ_N N > δ



≤ exp(−c4N ) , (1.15)

where c₄ is a positive constants. Thus for ε < ε^a_c the typical paths of the a-model touch the defect line only o(N ) times: when this happens it is customary to say that the model is delocalized.

What is left out from this analysis is the critical regime ε = ε^a_c. The behavior of the model in this case is sharply linked to the way in which the free energy f^a(ε) vanishes as ε ↓ ε^ac. If f^a(·) is differentiable also at ε = ε^ac (second order transition), then (f^a)^′(ε^a_c) = 0 and relation (1.15) holds, i.e. the a-model for ε = ε^a_c is delocalized. The other possibility is that f^a(·) is not differentiable at ε = ε^ac (first order transition), which happens when the right-derivative is positive: (f^a)^′₊(ε^a_c) > 0. In this case the behavior of P^a_ε,N for large N depends strongly on the choice of the boundary conditions.

We first consider the critical regime for the wetting model, where the transition turns out to be of first order. Recall the definition (1.13) of d^a_N(ε).

Theorem 1.3 (Critical wetting model). For the wetting model we have:

lim inf

N →∞ d^w_N ε^w_c

> 0 . (1.16)

Therefore (f^w)^′₊(ε^w_c ) > 0 and the phase transition is of first order.

Notice that equations (1.13) and (1.16) yield lim inf

N →∞

E^w_εw

c,N

ℓ_N N



> 0 ,

and in this sense the wetting model at the critical point exhibits a localized behavior. This is in sharp contrast with the gradient case, where it is well known that the wetting model at criticality is delocalized and in fact the transition is of second order, cf. [15, 18, 13, 11].

The emergence of a first order transition in the case of Laplacian interaction is particularly interesting in view of the possible applications of P^w_ε,Nas a model for the DNA denaturation transition, where the non-negative field {ϕⁱ}ⁱdescribes the distance between the two DNA strands. In fact for the DNA denaturation something close to a first order phase transition is experimentally observed: we refer to [17, §1.4] for a detailed discussion (cf. also [27, 19]).

Finally we consider the critical pinning model, where the transition is of second order.

Theorem 1.4 (Critical pinning model). For the pinning model we have:

lim sup

ε↓ε^pc

lim sup

N →∞

d^p_N(ε) = 0 . (1.17)

Then f^p(ε) is differentiable at ε = ε^p_c, (f^p)^′(ε^p_c) = 0 and the transition is of second order.

Although the relation (f^p)^′(ε^pc) = 0 yields ℓN = o(N ), in a delocalized fashion, the pinning model at ε = ε^p_c is actually somewhat borderline between localization and delocalization, as we point out in the next paragraph.

1.3. Further path results. A direct application of the techniques that we develop in this paper yields further path properties of the field. Let us introduce the maximal gap

∆_N := max

n ≤ N : ϕk+1 6= 0, ϕk+26= 0, . . . , ϕk+n6= 0 for some k ≤ N − n . One can show that, for both a ∈ {p, w} and for ε > ε^ac, the following relations hold:

∀δ > 0 : lim

N →∞

P^a_ε,N

∆_N N ≥ δ



= 0

L→∞lim lim sup

N →∞

i=1,...,N −1max

P^a_ε,N |ϕi| ≥ L

= 0 .

(1.18)

In particular for ε > ε^a_c each component ϕi of the field is at finite distance from the defect line and this is a clear localization path statement. On the other hand, in the pinning case a = p we can strengthen (1.15) to the following relation: for every ε < ε^p_c

L→∞lim lim sup

N →∞

P^p

ε,N ∆_N ≤ N − L

= 0 , (1.19)

i.e. for ε < ε^p_c the field touches the defect line at a finite number of sites, all at finite distance from the boundary points {0, N}. We expect that the same relation holds true also in the wetting case a = w, but at present we cannot prove it: what is missing are more precise estimates on the entropic repulsion problem, see §1.5 for a detailed discussion. It is interesting to note that we can prove that the first relation in (1.18) holds true also in the pinning case a = p at the critical point ε = ε^p_c, and this shows that the pinning model at criticality has also features of localized behavior.

We do not give an explicit proof of the above relations in this paper, both for conciseness and because in a second paper [9] we focus on the scaling limits of the pinning model, obtaining (de)localization path statements that are much more precise than (1.18) and (1.19) (under stronger assumptions on the potential V (·)). We show in particular that for all ε ∈ (0, ε^pc) the natural rescaling of P^p_ε,N converges in distribution in C([0, 1]) to the same limit that one obtains in the free case ε = 0, i.e. the integral process of a Brownian bridge. On the other hand, for every ε ≥ ε^pc the natural rescaling of P^p_ε,N yields the trivial process which is identically zero. We stress that ε = ε^pc is included in the last statement:

this is in sharp contrast with the gradient case, where the pinning model at criticality has a non-trivial scaling limit, namely the Brownian bridge (as one can prove arguing as in [13, 11]). This shows again the peculiarity of the critical pinning model in the Laplacian case. Indeed, by lowering the scaling constants with suitable logarithmic corrections, we are able to extract a non-trivial scaling limit (in a distributional sense) for the law P^p_εp

c,N

in terms of a symmetric stable L´evy process with index 2/5. We stress that the techniques and results of the present paper play a crucial role for [9].

1.4. Outline of the paper: approach and techniques. Although our main results are about the free energy, the core of our approach is a precise pathwise description of the field based on Markov renewal theory. In analogy to [13, 11] and especially to [10], we would like to stress the power of (Markov) renewal theory techniques for the study of (1 + 1)–dimensional linear chain models. The other basic techniques that we use are local limit theorems, an infinite-dimensional version of the Perron-Frobenius Theorem and the FKG inequality. Let us describe more in detail the structure of the paper.

In Section 2 we study the pinning and wetting models in the free case ε = 0, showing that these models are sharply linked to the integral of a random walk. More precisely, let {Yn}n≥0 denote a random walk starting at zero and with step law P(Y₁ ∈ dx) = exp(−V (x)) dx (the walk has zero mean and finite variance by (1.2)) and let us denote by Z_n:= Y₁+ . . . + Y_nthe corresponding integrated random walk process. In Proposition 2.2 we show that the law P^a_0,N is nothing but a bridge of length N of the process {Zn}n, with the further conditioning to stay non-negative in the wetting case a = w. Therefore we focus on the asymptotic properties of the process {Zn}n, obtaining a basic local limit theorem, cf. Proposition 2.3, and some polynomial bounds for the probability that {Zⁿ}ⁿ stays positive (connected to the problem of entropic repulsion that we discuss below, cf. §1.5).

In Section 3, which is in a sense the core of the paper, we show that for ε > 0 the law P^a_ε,N admits a crucial description in terms of Markov renewal theory. More precisely, we show that the zeros of the field {i ≤ N : ϕi= 0} under P^aε,Nare distributed according to the law of a (hidden) Markov renewal process conditioned to hit {N, N + 1}, cf. Proposition 3.1.

We thus obtain an explicit expression for the partition function Z_ε,N^a in terms of this Markov renewal process, which is the key to our main results.

Section 4 is devoted to proving some analytical results that underlie the construc- tion of the Markov renewal process appearing in Section 3. The main tool is an infinite- dimensional version of the classical Perron-Frobenius Theorem, cf. [33], and a basic role is played by the asymptotic estimates obtained in Seciton 2. A by-product of this analysis is an explicit formula, cf. (4.10), that links f^a(·) and ε^ac to the spectral radius of a suitable integral operator and that will be exploited later.

Sections 5, 6 and 7 contain the proofs of Theorems 1.2, 1.3 and 1.4 respectively. In view of the description given in Section 3, all the results to prove can be rephrased in the language of Markov renewal theory. The proofs are then carried out exploiting the asymptotic estimates derived in Sections 2 and 4 together with some algebraic manipulation of the kernel that gives the law of the hidden Markov renewal process (we refer to §1.6 for notation on kernels). Finally, the Appendixes contain the proof of some technical results.

1.5. Entropic repulsion. We recall that {Yn}n≥0, P

is the random walk with step P Y₁ ∈ dx

= e^{−V (x)}dx and that Z_n= Y₁+ . . . + Y_n. The analysis of the wetting model requires estimating the decay as N → ∞ of the probabilities P Ω⁺_N

and P Ω⁺_N

 Z_{N +1}= 0, Z_{N +2}= 0

, where we set Ω⁺_N :=

Z₁ ≥ 0, . . . , ZN ≥ 0

. This type of problem is known in the literature as entropic repulsion and it has received a lot of attention, see [32] for a recent overview. In the Laplacian case that we consider here, this problem has been solved in the Gaussian setting (i.e. when V (x) ∝ x²) in (d + 1)–dimension with d ≥ 5, cf. [28, 22].

Little is known in the (1+1)–dimensional setting, apart from the following result of Sinai’s [30] in the special case when {Yⁿ}ⁿ is the simple random walk on Z:

c

N^1/4 ≤ P Ω⁺N

≤ C

N^1/4, (1.20)

where c, C are positive constants. The proof of this bound relies on the exact combinatorial results available in the simple random walk case and it appears difficult to extend it to our situation. We point out that the same exponent 1/4 appears in related continuous models dealing with the integral of Brownian motion, cf. [23, 24]. Based on Sinai’s result, which we believe to hold for general random walks with zero mean and finite variance, we expect

that for the bridge case one should have the bound c

N^1/2 ≤ P Ω⁺N

 ZN +1= 0, ZN +2= 0

≤ C

N^1/2. (1.21)

We cannot derive precise bounds as (1.20) and (1.21), however for the purpose of this paper the following weaker result suffices:

Proposition 1.5. There exist positive constants c, C, c₋, c₊ such that for every N ∈ N c

N^c− ≤ P Ω⁺_N

≤ C

N^c+ (1.22)

c

N^c− ≤ P Ω⁺_N

 Z_{N +1}= 0, Z_{N +2}= 0

≤ C

N^c+ . (1.23)

We prove this proposition in Appendix C. We point out that the most delicate point is the proof of the upper bound in (1.22): the idea is to dilute the system on exponentially spaced times and then to combine FKG arguments with a suitable invariance principle.

While the value of c₊ that we obtain is non-optimal, our approach has the advantage of being quite robust: in view of the possible interest, we carry out the proof in a very general setting, namely we only assume that the random walk {Yn}nis in the domain of attraction of a stable law with positivity parameter ρ ∈ (0, 1). We point out that the same kind of arguments have found a recent application in [29].

1.6. Some recurrent notations. Throughout the paper, generic positive and finite con- stants will be denoted by (const.), (const.^′). For us N = {1, 2, . . .}, Z⁺ = N ∪ {0} and R⁺:= [0, ∞). Given two positive sequences (aⁿ), (bn), by an∼ bⁿwe mean that an/bn→ 1 as n → ∞. For x ∈ R we denote as usual by ⌊x⌋ := max{n ∈ Z : n ≤ x} its integer part.

In this paper we deal with kernels of two kinds. Kernels of the first kind are just σ–finite kernels on R, i.e. functions A_·,·: R×B(R) → R⁺, where B(R) denotes the Borel σ–field of R, such that A_x,·is a σ–finite Borel measure on R for every x ∈ R and A·,F is a Borel function for every F ∈ B(R). Given two such kernels Ax,dy, B_x,dy, their composition is denoted as usual by (A ◦ B)x,dy := R

z∈RA_x,dzB_z,dy and A^◦k_x,dy denotes the k-fold composition of A with itself, where A^◦0_x,dy := δ_x(dy). We also use the standard notation

(1 − A)⁻¹_x,dy :=

X∞ k=0

A^◦k_x,dy, which of course in general may be infinite.

The second kind of kernels is obtained by letting a kernel of the first kind depend on the further parameter n ∈ Z⁺, i.e. we consider objects of the form Ax,dy(n) with x, y ∈ R and n ∈ Z⁺. Given two such kernels Ax,dy(n), Bx,dy(n), we define their convolution by

(A ∗ B)x,dy(n) :=

Xn m=0

A(m)◦ B(n − m)

x,dy = Xn m=0

Z

z∈RAx,dz(m) · Bz,dy(n − m) , and the k-fold convolution of the kernel Ax,dy(n) with itself will be denoted by A^∗k_x,dy(n), where by definition A^∗0_x,dy(n) := δ₀(dy) 1_(n=0). Finally, given two kernels Ax,dy(n) and B_x,dy and a positive sequence (a_n), we will write

Ax,dy(n) ∼ B_x,dy

a_n (n → ∞)

to mean Ax,F(n) ∼ Bx,F/a_n as n → ∞, ∀x ∈ R and for every bounded Borel set F ⊂ R.

2. The free case ε = 0 : a random walk viewpoint

In this section we study in detail the free laws P^p_0,N and P^w_0,N and their link with the integral of a random walk. The main results are a basic local limit theorem and some asymptotic estimates.

2.1. Integrated random walk. Given a, b ∈ R, let (Ω, F, P = P^(a,b)) be a probability space on which are defined the processes {Xⁱ}i∈N, {Yⁱ}i∈Z⁺ and {Zⁱ}i∈Z⁺ with the following properties:

• {Xi}i∈Nis a sequence of independent and identically distributed random variables, with marginal laws X₁ ∼ exp(−V (x)) dx. We recall that by our assumptions on V (·) it follows that E(X1) = 0 and E(X₁²) = σ²∈ (0, ∞), cf. (1.2).

• {Yi}_i∈Z⁺ is the random walk associated to {Xi}, with starting point a, that is Y₀ = a Y_n= a + X₁+ . . . + X_n (2.1)

• {Zi}_i∈Z⁺ is the integrated random walk process with initial value b: that is Z₀ = b and for n ∈ N

Z_n = b + Y₁+ . . . + Y_n = b + na + nX₁+ (n − 1)X2+ . . . + X_n. (2.2) From (2.1) and (2.2) it follows that

 Yn, Zn

nunder P^(a,b) =^d 

Yn+ a , Zn+ b + na

n under P^(0,0). (2.3) The marginal distributions of the process {Zn}n are specified in the following lemma.

Lemma 2.1. For every n ∈ N, the law of the vector (Z1, . . . , Z_n) under P^(a,b) is given by P^(a,b) (Z₁, . . . , Z_n) ∈ (dz1, . . . , dz_n)

= exp −H_[−1,n](z₋₁, z₀, z₁, . . . , z_n)
Yⁿ

i=1

dz_i, (2.4) where we set z₋₁ := b − a and z0 := b.

Proof. By definition Yn= Zn− Zn−1 for n ≥ 1 under P^(a,b). Then, setting yi:= zi− zi−1

for i ≥ 2 and y1 := z₁ − b, it suffices to show that, under the measure given by the r.h.s. of (2.4), the variables (yi)i=1,...,n are distributed like the first n steps of a random walk starting at a and with step law exp(−V (x)) dx. But for this it suffices to rewrite the Hamiltonian as

H[−1,n](z) = V (z1− b) − (b − (b − a))
+

n−1X

i=1

V (zi+1− zⁱ) − (zⁱ− zi−1)

= V y₁− a
+

n−1X

i=1

V y_i+1− yi

,

and the proof is completed. 

By construction {(Yⁿ, Zn)}n∈Z⁺ under P^(a,b) is a Markov process with starting values Y₀= a, Z₀= b. On the other hand, the process {Zn}n alone is not a Markov process: it is rather a process with finite memory m = 2, i.e. for every n ∈ N

P^(a,b) {Zn+k}k≥0 ∈ ·

 Z_i, i ≤ n

= P^(a,b) {Zn+k}k≥0 ∈ ·

 Z_n−1, Z_n

= P^{(Zn−Zn−1,Zn)} {Zk}k≥0 ∈ ·

, (2.5)

as it follows from Lemma 2.1. For this reason the law P^(a,b) may be viewed as P^(a,b) = P · 

 Z₋₁= b − a, Z⁰= b

. (2.6)

2.2. The link with P^a_0,N. In the r.h.s. of (2.4) we see exactly the same density appearing in the definitions (1.5) and (1.7) of our models P^a_0,N. As an immediate consequence we have the following proposition, which states that P^a_0,N is nothing but a bridge of the process {Zn}n for a = p, with the further constraint to stay non-negative for a = w.

Proposition 2.2. The following statements hold:

(1) The pinning model P^p_0,N is the law of the vector (Z₁, . . . , Z_{N −1}) under the measure P^(0,0)( · | ZN = 0, Z_{N +1}= 0). The partition function Z0,N^p is the value at (0, 0) of the density of the vector (Y_{N +1}, Z_{N +1}) under the law P^(0,0).

(2) Setting Ω⁺_n := {S1 ≥ 0, . . . , Sn≥ 0}, the wetting model P^w0,N is the law of the vector (Z₁, . . . , Z_{N −1}) under the measure P^(0,0)( · | Ω⁺_{N −1}, Z_N = 0, Z_{N +1}= 0). The parti- tion function Z0,N^w is the value at (0, 0) of the density of the vector (Y_{N +1}, Z_{N +1}) under the law P^(0,0)( · | Ω⁺_{N −1}).

For the statement on the partition function, observe that the density at (0, 0) of the vector (YN +1, ZN +1) coincides with the one of the vector (ZN, ZN +1), since YN +1= ZN +1− Z^N. 2.3. A local limit theorem. In view of Proposition 2.2, we study the asymptotic be- havior as n → ∞ of the vector (Yn, Z_n) under the law P^(a,b).

Let us denote by {B^t}t∈[0,1] a standard Brownian motion and by {I^t}t∈[0,1] its integral process I_t:=Rt

0B_sds. A simple application of Donsker’s invariance principle shows that the vector Y_n/(σ√

n), Z_n/(σn^3/2)

under P^(0,0) converges in distribution as n → ∞ toward the law of the centered Gaussian vector (B1, I1), whose density g(y, z) is

g(y, z) = 6

π exp − 2y²− 6z²+ 6yz

. (2.7)

We want to reinforce this convergence in the form of a local limit theorem. To this purpose, we introduce the density of (Y_n, Z_n) under P^(a,b), setting for n ≥ 2

ϕ^(a,b)_n (y, z) = P^(a,b) (Y_n, Z_n) ∈ (dy, dz)

dy dz . (2.8)

From (2.3) it follows that

ϕ^(a,b)_n (y, z) = ϕ^(0,0)_n y − a , z − b − na

, (2.9)

hence it suffices to focus on ϕ^(0,0)n (·, ·). We set for short ϕ^(0,0)n (R, z) := R

Rϕ^(0,0)n (y, z) dy, i.e. the density of Z_n under P^(0,0), and g(R, z) :=R

Rg(y, z) dy. We are ready to state the main result of this section.

Proposition 2.3 (Local limit theorem). The following relations hold as n → ∞:

sup

(y,z)∈R²

 σ²n²ϕ^(0,0)_n σ√

n y , σn^3/2z

− g(y, z)  → 0 sup

z∈R

 σn^3/2ϕ^(0,0)_n R, σn^3/2z

− g(R, z)

 → 0 . (2.10)

The proof, based on Fourier analysis, is deferred to Appendix B. We stress that this result retains a crucial importance in the rest of the paper. Notice that an analogous local limit theorem holds also for ϕ^(0,0)_n (y, R), i.e. the density of Y_n, but we do not state it explicitly because we will not need it.

2.4. The positivity constraint. To deal with the wetting model we need to study the law of the random vector (Y_n, Z_n), or equivalently of (Z_n−1, Z_n), conditionally on the event Ω⁺_n−2= {Z1 ≥ 0, . . . , Zn−2 ≥ 0}. To this purpose we set for x, y ∈ R and n ≥ 3

w_x,y(n) := 1_{(x≥0, y≥0)}· P^(−x,0) Ω⁺_n−2

 Z_n−1= y, Z_n= 0

, (2.11)

while for n = 1, 2 we simply set wx,y(n) := 1_{(x≥0,y≥0)}. We are interested in the rate of decay of w_x,y(n) as n → ∞. To this purpose we claim that there exists a positive constant c₊ such that the following upper bound holds: for all n ∈ N and x, y ∈ R⁺

w_x,y(n) ≤ (const.)

n^c+ · 1

P^(0,0) Z_n−1 ≥ y + (n − 1)x, Zn≥ nx
. (2.12) Moreover we have the following lower bound for x, y = 0 and ∀n ∈ N:

w_0,0(n) ≥ (const.)

n^c− , (2.13)

for some positive constant c₋. Notice that by Proposition 2.2 we have Z0,N^p = ϕ^(0,0)_{N +1}(0, 0) and Z0,N^w = Z0,N^p · w0,0(N + 1), hence by (2.10) and (2.13) we have for every N ∈ N

Z_0,N^p ≥ Z0,N^w ≥ (const.)

N^2+c− , (2.14)

so that the last inequality in (1.9) is proven.

We prove the lower bound (2.13) in Appendix C.1: the idea is to restrict the expectation that defines w_0,0(n) on a suitable subset of paths, whose probability can be estimated. On the other hand, the upper bound (2.12) follows directly combining the following Lemma with the upper bound in (1.22) (which is proven in Appendix C.2).

Lemma 2.4. For every x, y ∈ R⁺ and n ∈ N we have w_x,y(n) ≤ P^(0,0) Ω⁺_n

· 1

P^(0,0) Z_n−1≥ y + (n − 1)x, Zn ≥ nx
. (2.15) Proof. It is convenient to denote by p_n the image law on Rⁿ of the vector (X₁, . . . , X_n) under P^(0,0), i.e. p_n(dt₁, . . . , dt_n) = Qn

i=1e^{−V (ti)}dt_i. With some abuse of notation, for t = (t₁, . . . , t_n) ∈ Rⁿ and x ∈ R we set Zi(t) := −ix + it1+ (i − 1)t2+ . . . + t_i, so that the process {Zi(t)}1≤i≤n under p_n(dt) is distributed like the process {Zi}1≤i≤nunder P^(−x,0). Since p_n is an i.i.d. law and the event {Zn−1(t) ≥ y, Zn(t) ≥ 0} is increasing in t, the conditioned law p^∗_n:= p_n( · | Zn−1 ≥ y, Zn≥ 0) satisfies the FKG inequality, cf. [26]. This means that for Borel sets A, B ⊆ Rⁿ such that A is increasing and B is decreasing, we have p^∗_n(A | B) ≤ p^∗n(A). The choices A = Ω⁺_n and B := {Zn−1≤ y + ε, Zⁿ≤ ε} yield

P^(−x,0) Ω⁺_n

 Z_n−1∈ [y, y + ε], Zn∈ [0, ε]

≤ P^(−x,0)(Ω⁺_n| Zn−1 ≥ y, Zn≥ 0) . The conclusion follows letting ε ↓ 0 and noting that P^(−x,0) Z₁ ≥ 0, . . . , Zn ≥ 0

is decreasing in x and P^(−x,0)(Z_n−1 ≥ y, Zn ≥ 0) = P^(0,0) Z_n−1 ≥ y + (n − 1)x, Zn ≥ nx

by relation (2.3). 

3. The interacting case ε > 0: a renewal theory description

In this section we study in detail the laws P^p_ε,N and P^w_ε,N in the case ε > 0. The crucial result is that the contact set {i ∈ Z⁺ : ϕ_i = 0} can be described in terms of a Markov renewal process. Throughout the section we assume that ε > 0.

3.1. The law of the contact set. We introduce the contact set τ by:

τ := 

i ∈ Z⁺ : ϕ_i = 0

⊂ Z⁺, (3.1)

where we set by definition ϕ0 = 0, so that 0 ∈ τ. It is practical to identify the set τ with the increasing sequence of random variables {τk}k≥0 defined by

τ0:= 0 τ_k+1:= inf{i > τk: ϕi = 0} . (3.2) Observe that the random variable ℓ_N, introduced in (1.12), may be expressed as ℓ_N :=

max{k : τk≤ N}. Next we introduce the process {Jk}k≥0 that gives the height of the field before the contact points:

J0 := 0 J_k:= ϕτ_k−1. (3.3)

The basic observation is that the joint law of the process {ℓN, (τ_k)_k≤ℓN, (J_k)_k≤ℓN} under P^a

ε,N can be written in the following ‘product form’: for k ∈ N, for (tⁱ)_i=1,...,k ∈ N with 0 < t1 < . . . < t_k−1< t_k := N and for (yi)_i=1,...,k∈ R we have

P^a_ε,N



ℓ_N = k, τ_i = t_i, J_i ∈ dyi, i = 1, . . . , k

= 1

Zε,N^a

ε^k−1F^a_0,dy1(t1) · F^ay1,dy2(t2− t¹) · . . . · F^ay_k−1,dy_k(N − tk−1) · F^ayk,{0}(1) , (3.4) for a suitable kernel F^a_x,dy(n) that we now define. For a = p we have

F^p_x,dy(n) :=



















e^{−H[−1,1](x,0,0)}δ₀(dy) = e^{−V (x)}δ₀(dy) if n = 1 e^{−H[−1,2](x,0,y,0)}dy = e−V (x+y)−V (−2y)dy if n = 2

 Z

Rn−2

e^{−H[−1,n](ϕ−1,...,ϕn)}dϕ₁· · · dϕn−2

 dy where ϕ₋₁ = x, ϕ₀ = 0, ϕ_n−1= y, ϕ_n= 0





 if n ≥ 3

, (3.5)

and the definition of F^w_x,dy(n) is analogous: we just have to impose that x, y ≥ 0 and for n ≥ 3 we also have to restrict the integral in (3.5) on (R⁺)ⁿ⁻². Although these formulas may appear quite involved, they follow easily from the definition of P^a_ε,N. In fact it suffice to expand the product of measures in the r.h.s. of (1.5) and (1.7) as a sum of ‘monomials’, according to the elementary formula (where we set Ω^p:= R and Ω^w := R⁺)

N −1Y

i=1

εδ₀(dϕ_i) + dϕ_i1_(ϕi∈Ω^a₎

= X

A⊂{1,...,N−1}

ε^|A| Y

m∈A

δ₀(dϕ_m) Y

n∈A^∁

dϕ_n1_(ϕn∈Ω^a₎.

It is then clear that A = {τ1, . . . , τ_ℓN−1} and integrating over the variables ϕi with index i 6∈ A ∪ (A − 1) one gets to (3.4). We stress that the algebraic structure of (3.4) retains a crucial importance, that we are going to exploit in the next paragraph.

From (3.5) it follows that the kernel F^a_x,dy(n) is a Dirac mass in y for n = 1 while it is absolutely continuous for n ≥ 2. Then it is convenient to introduce the σ–finite measure µ(dx) := δ₀(dx) + dx, so that we can write

F^a_x,dy(n) = f_x,y^a (n) µ(dy) . (3.6) The interesting fact is that the density fx,y^a (n) can be rephrased explicitly in terms of the process {Zk}k introduced in §2.1. Let us start with the pinning case a = p: from Lemma 2.1 and from equation (3.5) it follows that, for n ≥ 2, f^x,yp (n) is nothing but the density of (Z_n−1, Z_n) at (y, 0), under P^(−x,0). Recalling the definition (2.8) of ϕ^(·,·)_n (·, ·) and the fact that Z_n−1= Zn− Yⁿ, we can write

fx,y^p (n) =

(e^{−V (x)}1_(y=0) if n = 1

ϕ^(−x,0)n (−y, 0) 1_(y6=0) = ϕ^(0,0)n (−y + x, nx) 1_(y6=0) if n ≥ 2 , (3.7) where we have used relation (2.9). Analogously, recalling the definition (2.11) of w_x,y(n), in the wetting case we have

f_x,y^w (n) = f_x,y^p (n) · wx,y(n) . (3.8) Equations (3.6), (3.7) and (3.8) provide a description of the kernel F^a_x,dy(n) which is both simpler and more useful than the original definition (3.5).

3.2. A Markov renewal theory interpretation. Equation (3.4) expresses the law of {(τk, J_k)}k under P^a_ε,N in terms of an explicit kernel F^a_x,dy(n). The crucial point is that the algebraic structure of the equation (3.4) allows to modify the kernel, in order to give this formula a direct renewal theory interpretation. In fact we set

K^a,ε_x,dy(n) := ε F^ax,dy(n) e^−fa(ε)n v^a_ε(y)

v^a_ε(x), (3.9)

where the number f^a(ε) ∈ [0, ∞) and the positive real function v^aε(·) will be defined explicitly in Section 4. Of course this is an abuse of notation, because the symbol f^a(ε) was already introduced to denote the free energy, cf. (1.8), but we will show in §5.2 that the two quantities indeed coincide. We denote by k^a,εx,y(n) the density of K^a,ε_x,dy(n) with respect to µ(dy), i.e.

k^a,εx,y(n) := ε fx,y^a (n) e^−fa(ε)n v_ε^a(y)

v^a_ε(x). (3.10)

The reason for introducing the kernel K^a,ε_x,dy(n) lies is the following fundamental fact: the number f^a(ε) and the function v^a_ε(·) appearing in (3.9) can be chosen such that:

∀x ∈ R : Z

y∈R

X

n∈N

K^a,ε_x,dy(n) = ε

ε^a_c ∧ 1 ≤ 1 , (3.11) where ε^a_c ∈ (0, ∞) is a fixed number. A detailed proof and discussion of this fact, with an explicit definition of ε^a_c, f^a(ε) and v_ε^a(·), is deferred to Section 4: for the moment we focus on its consequences.

Thanks to (3.11), we can define the law Pε^a under which the joint process {(τk, J_k)}k≥0

is a (possibly defective) Markov chain on Z⁺× R, with starting value (τ0, J₀) = (0, 0) and with transition kernel given by

Pε^a (τ_k+1, J_k+1) ∈ ({n}, dy)

 (τk, J_k) = (m, x)

= K^a,ε_x,dy(n − m) . (3.12) An alternative (and perhaps more intuitive) definition is as follows:

• First sample the process {Jk}k≥0 as a (defective if ε < ε^a_c) Markov chain on R, with J₀ = 0 and with transition kernel

Pε^a( J_k+1 ∈ dy | Jk= x ) = X

n∈N

K^a,ε_x,dy(n) =: D^a,ε_x,dy. (3.13) In the defective case we take ∞ as cemetery.

• Then sample the increments {Tk := τ_k− τk−1}k∈N as a sequence of independent, but not identically distributed, random variables, according to the conditional law:

Pε^a( T_k= n | {Ji}i≥0) =











1_(n=1) if J_k= 0

k^a,ε_Jk−1,Jk(n) 1(n≥2)

P

m≥2k^a,ε_Jk−1,Jk(m) if J_k6= 0, Jk 6= ∞

1_(n=∞) if J_k= ∞

.

We stress that the process {(τk, J_k)}k≥0 is defective if ε < ε^a_c and proper if ε ≥ ε^ac, cf. (3.11). The process {τk}k≥0 under Pε^a is what is called a Markov renewal process and {Jk}k≥0 is its modulating chain. This is a generalization of classical renewal processes, because τ_n = T₁ + . . . + T_n where the variables {Tk}k∈N are allowed to have a special kind of dependence, namely they are independent conditionally on the modulating chain {Jk}k≥0. For a detailed account on Markov renewal processes we refer to [3].

Now let us come back to equation (3.4). We perform the substitution F^a_x,dy(n) → K^a,ε_x,dy(n), defined in (3.9): the boundary terms v^a_ε(y)/v_ε^a(x) get simplified and the ex- ponential term e^−fa(ε)n factorizes, so that we get

P^a_ε,N



ℓ_N = k, τ_i= t_i, J_i ∈ dyi, i = 1, . . . , k

= e^fa(ε)N

ε²Z_ε,N^a K^a,ε_0,dy1(t₁) · K^a,εy1,dy2(t₂− t1) · . . . · K^a,εy_k−1,dy_k(N − tk−1) · K^a,ε_yk,{0}(1) . (3.14)

Moreover, since the partition function Zε,N^a is the normalizing constant that makes P^a_ε,N a probability, it can be expressed as

Zε,N^a = e^fa(ε)N ε²

XN k=1

X

ti∈N, i=1,...,k 0<t1<...<t_k:=N

Z

Rk

Yk i=1

K^a,ε_yi−1,dyi(t_i− ti−1)

!

· K^a,ε_yk,{0}(1) . (3.15)

We are finally ready to make explicit the link between the law Pε^a and our model P^a_ε,N. Let us introduce the event

A^N := 

{N, N + 1} ⊂ τ

= 

∃k ≥ 0 : τk= N, τ_k+1 = N + 1

. (3.16)

The following proposition is an immediate consequence of (3.12), (3.14) and (3.15).

Proposition 3.1. For any N ∈ N and ε > 0, the vector 

ℓ_N, (τ_i)_i≤ℓN, (J_i)_i≤ℓN

has the same law under P^a_ε,N and under the conditional law Pε^a( · | AN): for all k, {ti}i and {yi}i

P^a

ε,N



ℓ_N = k, τ_i= t_i, J_i ∈ dyi, i ≤ k

= Pε^a



ℓ_N = k, τ_i = t_i, J_i ∈ dyi, i ≤ k  A^N

 . Moreover the partition function can be expressed as Zε,N^a = (e^fa(ε)N/ε²) · Pε^a(AN).

Thus we have shown that the contact set τ ∩ [0, N] under the pinning law P^aε,N is distributed like a Markov renewal process (of law Pε^a) conditioned to visit {N, N +1}. The crucial point is that Pε^a does not have any dependence on N , therefore all the dependence on N of P^a_ε,N is contained in the conditioning on the event A^N. As it will be clear in the next sections, this fact is the key to all our results.

4. An infinite dimensional Perron-Frobenius problem

In this section we prove that for every ε > 0 the non-negative number f^a(ε) and the positive real function v_ε^a(·) : R → (0, ∞) appearing in the definition of K^a,ε_x,dy(n), cf. (3.9), can be chosen in such a way that equation (3.11) holds true.

4.1. Some analytical preliminaries. We recall that the kernel F^a_x,dy(n) and its density fx,y^a (n) are defined in equations (3.6), (3.7) and (3.8), and that µ(dx) = δ₀(dx) + dx. In particular we have 0 ≤ fx,y^w (n) ≤ fx,y^p (n). We first list some important properties of fx,y^p (n):

• Uniformly for x, y in compact sets we have:

f_x,y^p (n) ∼ c

n² (n → ∞) , (4.1)

where c := 6/(πσ²); moreover there exists C > 0 such that ∀x, y ∈ R and n ∈ N f_x,y^p (n) ≤ C

n²

Z

R

dz f_x,z^p (n) ≤ C n^3/2

Z

R

dz f_z,y^p (n) ≤ C

n^3/2. (4.2) Both the above relations follow comparing (3.7) with Proposition 2.3.

• For n ≥ 2 we have: Z

R2dx dy fx,y^p (n) = 1

n, (4.3)

as it follows from (3.7) recalling that ϕ^(0,0)_n (·, ·) is a probability density.

For λ ≥ 0 we introduce the kernel

B_x,dy^a,λ :=X

n∈N

e^−λnF^ax,dy(n) , (4.4)

which induces the integral operator: (B^a,λh)(x) :=R

RB^a,λ_x,dyh(y). Note that for every x ∈ R the kernel B_x,dy^a,λ is absolutely continuous with respect to the measure µ, so that we can write B^a,λ_x,dy = b^a,λ(x, y) µ(dy), where the density b^a,λ(x, y) is given by (cf. (3.7))

b^a,λ(x, y) = e^−λf_x,0^a (1) 1_(y=0) + X

n≥2

e^−λnf_x,y^a (n) 1_(y6=0). (4.5) The following result is of basic importance.

Lemma 4.1. For every λ ≥ 0, B^a,λ is a compact operator on the Hilbert space L²(R, dµ).

Proof. We are going to check the stronger condition that B^a,λ is Hilbert-Schmidt, i.e.

Z

R2

b^a,λ(x, y)²µ(dx) µ(dy) < ∞ . (4.6)