2007, Vol. 17, No. 4, 1362–1398 DOI:10.1214/105051607000000159

©Institute of Mathematical Statistics, 2007

**A RENEWAL THEORY APPROACH TO PERIODIC COPOLYMERS**
**WITH ADSORPTION**

BY FRANCESCOCARAVENNA, GIAMBATTISTAGIACOMIN^{1}
AND LORENZO ZAMBOTTI

*Università degli Studi di Padova, Université Paris 7 and Université Paris 6*

We consider a general model of a heterogeneous polymer chain fluctu-
ating in the proximity of an interface between two selective solvents. The
*heterogeneous character of the model comes from the fact that the monomer*
*units interact with the solvents and with the interface according to some*
*charges that they carry. The charges repeat themselves along the chain in*
a periodic fashion. The main question concerning this model is whether the
*polymer remains tightly close to the interface, a phenomenon called localiza-*
*tion, or whether there is a marked preference for one of the two solvents, thus*
*yielding a delocalization phenomenon.*

In this paper, we present an approach that yields sharp estimates for the
partition function of the model in all regimes (localized, delocalized and crit-
ical). This, in turn, makes possible a precise pathwise description of the poly-
*mer measure, obtaining the full scaling limits of the model. A key point is*
*the closeness of the polymer measure to suitable Markov renewal processes,*
Markov renewal theory being one of the central mathematical tools of our
analysis.

**1. Introduction and main results.**

*1.1. Two motivating models. Let S:= {S**n*}*n=0,1,...**be a random walk, S*_{0}= 0
*and S**n*=^{}^{n}_{j}_{=1}*X**j*, with i.i.d. symmetric increments taking values in*{−1, 0, +1}.*

*Hence the law of the walk is identified by p := P(X*1

*1*

**= 1) = P(X***= −1) and we*

*assume that p∈ (0, 1/2). The case p = 1/2 can be treated in an analogous way,*but requires some notational care because of the periodicity of the walk. We also

*consider a sequence ω:= {ω*

*n*}

*n*

*∈N={1,2,...}*of real numbers with the property that

*ω*

*n*

*= ω*

*n*

*+T*

*for some T*

*∈ N and for every n, denoting by T (ω) the minimal value*

*of T .*

Consider the following two families of modifications of the law of the walk,
*both indexed by a parameter N*∈ N:

Received March 2006; revised March 2006.

1Supported in part by ANR project, POLINT BIO.

*AMS 2000 subject classifications.*60K35, 82B41, 82B44.

*Key words and phrases. Random walks, renewal theory, Markov renewal theory, scaling limits,*
polymer models, wetting models.

1362

*Pinning and wetting models.* *For λ***≥ 0, consider the probability measure P***N,ω*

defined by

**dP***N,ω*

**dP** *(S)*∝ exp

*λ*

*N*
*n*=1

*ω**n***1**_{{S}_{n}_{=0}}

*.*
(1.1)

*The walk receives a pinning reward, which may be negative or positive, each time*
it visits the origin. By considering the directed walk viewpoint, that is,*{(n, S**n**)*}*n*,
one may interpret this model in terms of a directed linear chain receiving an ener-
getic contribution when it touches an interface. The main question is whether for
**large N the typical paths of P***N,ω*are attracted or repelled by the interface.

There is an extensive literature on periodic pinning and wetting models, the
*majority of which is restricted to the T* = 2 case, for example, [13, 25]; see [15]

for further discussion and references.

*Copolymer near a selective interface.* In much the same way, we introduce
**dP**_{N,ω}

**dP** *(S)*∝ exp

*λ*

*N*
*n=1*

*ω**n**sign(S**n**)*

*,*
(1.2)

*where if S*_{n}*= 0, we set sign(S**n**):= sign(S**n*−1**)1**_{{S}_{n−1}_{=0}}. This convention for
*defining sign(0), to be kept throughout the paper, simply means that sign(S**n**)*=
*+1, 0, −1 according to whether the bond joining S**n*−1 *and S** _{n}* lies above, on or

*below the x-axis.*

**Also in this case, we take a directed walk viewpoint and P*** _{N,ω}*then may be in-
terpreted as a polymeric chain in which the monomer units, the bonds of the walk,

*are charged. An interface, the x-axis, separates two solvents, say oil above and wa-*ter below. Positively charged monomers are hydrophobic and negatively charged ones are instead hydrophilic. In this case, one expects competition between three possible scenarios: polymer preferring water, preferring oil or undecided between the two and choosing to fluctuate in the proximity of the interface.

We select [23, 27] from the physical literature on periodic copolymers, keeping in mind, however, plays that periodic copolymer modeling plays a central role in applied chemistry and material science.

*1.2. A general model. We point out that the models presented in Section*1.1
are particular examples of the polymer measure with Hamiltonian

H*N**(S)*= ^{}

*i*=±1

*N*
*n*=1

*ω*^{(i)}_{n}**1**_{{sign(S}_{n}* _{)=i}}*+

*N*
*n*=1

*ω*_{n}^{(0)}**1**_{{S}_{n}_{=0}}

(1.3)

+

*N*
*n*=1

*ω*_{n}^{(0)}**1**_{{sign(S}_{n}*)*=0}*,*

*where ω*^{(}^{±1)}*, ω** ^{(0)}* and

*ω*

*are periodic sequences of real numbers. Observe that*

^{(0)}*by our convention on sign(0), the last term provides an energetic contribution (of*pinning/depinning type) to the bonds lying on the interface. We use the shorthand

*ω*for the four periodic sequences appearing in (1.3) and will use T

*= T (ω) to*

*denote the smallest common period of the sequences. We will refer to ω as to the*

*charges of our system.*

Besides being a natural model, generalizing and interpolating between pinning and copolymer models, the general model we consider is one which has been con- sidered on several occasions (see, e.g., [28] and references therein).

Starting from the Hamiltonian (1.3), for a*= c (constrained) or a = f (free), we*
**introduce the polymer measure P**^{a}* _{N,ω}*onZ

^{N}, defined by

**dP**^{a}_{N,ω}

**dP** *(S)*=*exp(H**N**(S))*
*Z*^{a}_{N,ω}

**1**_{{a=f}}**+ 1***{a=c}***1**_{{S}_{N}_{=0}}^{}*,*
(1.4)

where*Z*^{}^{a}_{N,ω}**:= E[exp(H***N***)(1**_{{a=f}}**+ 1***{a=c}***1**_{{S}_{N}_{=0}}*)] is the partition function, that*
**is, the normalization constant. Observe that the polymer measure P**^{a}* _{N,ω}*is invariant

*under the joint transformation S→ −S, ω*

^{(}

^{+1)}*→ ω*

^{(}*, hence, by symmetry, we may (and will) assume that*

^{−1)}*h** _{ω}*:= 1

*T (ω)*

*T (ω)*
*n*=1

*ω*^{(}_{n}^{+)}*− ω*^{(}_{n}^{−)}^{}*≥ 0.*

(1.5)

We also set*S := Z/(T Z) and for β ∈ S, we equivalently write [n] = β or n ∈ β.*

*Notice that the charges ω**n*are functions of*[n], so that we can write ω*_{[n]}*:= ω**n*.
*1.3. The free energy viewpoint. The standard statistical mechanics approach*
naturally leads to a consideration of the free energy of the model, that is, the limit
*as N* *→ ∞ of (1/N) logZ*^{}_{N,ω}* ^{a}* . It is, however, practical to observe that we can
add to the Hamiltonian H

*N*

*a term which is constant with respect to S without*changing the polymer measure. Namely, if we set

H_{N}^{} *(S)*:= H*N**(S)*−

*N*
*n*=1

*ω*^{(}_{n}^{+1)}*,*

*which amounts to sending ω*^{(+1)}*n* *→ 0, ω*^{(−1)}*n* *→ (ω*^{(−1)}*n* *− ω**n*^{(+1)}*)* and *ω*^{(0)}*n* →
*(ω*^{(0)}*n* *− ω*^{(+1)}*n* *), then we can write*

**dP**^{a}_{N,ω}

**dP** *(S)*=*exp(H*_{N}^{} *(S))*
*Z*^{a}_{N,ω}

**1**_{{a=f}}**+ 1***{a=c}***1**_{{S}_{N}_{=0}}^{}*,*
(1.6)

*where Z*_{N,ω}* ^{a}* is a new partition function given by

*Z*^{a}_{N,ω}**= E**^{}*exp(H*_{N}^{} *)*^{}**1**_{{a=f}}**+ 1***{a=c}***1**_{{S}_{N}_{=0}}^{ }=*Z*^{}_{N,ω}^{a}*· e*^{−}^{}^{N}^{n=1}^{ω}^{n}^{(}^{+1)}*.*
(1.7)

At this point, we define the free energy:

F* _{ω}*:= lim

*N*→∞

1

*N* *log Z*^{c}_{N,ω}*.*
(1.8)

A proof of the existence of such a limit involves standard superadditive arguments, as well as the fact that the superscript c could be changed to f without changing the result (see, e.g., [15], but a complete proof, without the use of super-additivity, is given below).

*The principle that the free energy contains the crucial information on the large N*
behavior of the system is certainly not violated in this context. In order to clarify
this point, let us first observe that F_{ω}*≥ 0 for every ω. This follows by observing*
that the energetic contribution to the trajectories that stay positive and come back
*to zero for the first time at epoch N is just ω*_{N}* ^{(0)}*, hence, by (1.7),

1

*N* *log Z*^{c}* _{N,ω}* ≥

*ω*

_{N}

^{(0)}*N*+ 1

*N* **log P(S***n**>0, n= 1, . . . , N − 1, S**N* *= 0)*
(1.9)

*N**−→ 0,*→∞

where we have simply used the fact that the distribution of the first return to zero
*of S is subexponential [see (2.2) for a much sharper estimate]. This suggests a*
natural dichotomy and, inspired by (1.9), we give the following definition.

DEFINITION1.1. The polymer chain defined by (1.4) is said to be

*• localized if*^{F}*ω**>*0;

*• delocalized if*^{F}*ω*= 0.

A priori, one is certainly not totally convinced by such a definition. Localization, as well as delocalization, should be given in terms of path properties of the process:

it is quite clear that the energyH_{N}^{} *(S)of trajectories S which do not come back*
very often (i.e., not in a positively recurrent fashion) to the interface will either be
*negative or o(N ), but this is far from being a convincing statement of localization.*

An analogous observation can be made for delocalized polymer chains.

Nonetheless, with a few exceptions, much of the literature focuses on free en- ergy estimates. For example, in [5], one can find the analysis of the free energy of a subset of the class of models we are considering here, and in Section 1.7 of the same work, it is argued that some (weak) path statements of localization and delocalization can be extracted from the free energy. We will return to a review of the existing literature after we have stated our main results, but at this point, we anticipate that our program is going well beyond free energy estimates.

One of the main results in [5] is a formula forF* _{ω}*, obtained via large deviations
arguments. We will not give the precise expression now (the reader can find it in
Section3.2below), but we point out that this formula is proved here using argu-
ments that are more elementary and, at the same time, these arguments yield much

*stronger estimates. More precisely, there exists a positive parameter δ** ^{ω}*, given ex-
plicitly and analyzed in detail in Section2.1, that determines the precise asymptotic

*behavior of the partition function (the link between δ*

*andF*

_{ω}*will be immediately after the statement).*

_{ω}THEOREM1.2 (Sharp asymptotic estimates). *Fix η∈ S and consider the as-*
*ymptotic behavior of Z*_{N,ω}^{c} *as N* *→ ∞ along [N] = η. Then:*

*(1) if δ*^{ω}*<1 then Z*^{c}_{N,ω}*∼ C**ω,η*^{<}*/N** ^{3/2}*;

*(2) if δ*

^{ω}*= 1 then Z*

^{c}

_{N,ω}*∼ C*

*ω,η*

^{=}

*/N*

*;*

^{1/2}*(3) if δ*^{ω}*>1 then*F_{ω}*>0 and Z*_{N,ω}^{c} *∼ C**ω,η*^{>}*exp(*F_{ω}*N ),*

*where the quantities*F_{ω}*, C*_{ω,η}^{>}*, C*_{ω,η}^{<}*and C*_{ω,η}^{=} *are given explicitly in Section*3.

*Of course, by a**N**∼ b**N* *we mean a**N**/b**N* → 1. We note that Theorem1.2implies
the existence of the limit in (1.8) and that F_{ω}*= 0 exactly when δ** ^{ω}* ≤ 1, but we
stress that in our arguments, we do not rely on (1.8) to defineF

*. We also point out that analogous asymptotic estimates can be obtained for the free partition function;*

_{ω}see Proposition3.2.

It is rather natural to think that from such precise estimates one can extract detailed information on the limit behaviors of the system. This is correct. Notably, we can consider:

**(1) infinite volume limits, that is, weak limits of P**^{a}* _{N,ω}*as a measure onR

^{N};

*(2) scaling limits, that is, limits in law of the process S, suitably rescaled, under*

**P**^{a}* _{N,ω}*.

*Here, we will focus only on (2); the case (1) is considered in [7].*

A word of explanation is in order concerning the fact that there appear to be two
*types of delocalized polymer chains: those with δ*^{ω}*= 1 and those with δ*^{ω}*<*1. As
we will see, these two cases exhibit substantially different path behavior (even if
both display distinctive features of delocalized paths, notably a vanishing density
*of visits at the interface). As will be made clear, in the case δ*^{ω}*<*1, the system
*is strictly delocalized in the sense that a small perturbation in the charges leaves*
*δ*^{ω}*<1 (as a matter of fact, for charges of a fixed period, the mapping ω→ δ** ^{ω}* is

*continuous), while δ*

^{ω}*is rather a borderline, or critical, case.*

*1.4. The scaling limits. The main results of this paper concern the diffusive*
**rescaling of the polymer measure P**^{a}* _{N,ω}*. More precisely, let us define the map

*X*

*:R*

^{N}

^{N}*→ C([0, 1]):*

*X*_{t}^{N}*(x)*= *x*_{Nt}

*σ N*^{1/2}*+ (Nt − Nt)x*_{Nt+1}*− x**Nt*

*σ N*^{1/2}*,* *t∈ [0, 1],*

where*· denotes the integer part of · and σ*^{2}*:= 2p is the variance of X*1 under
**the original random walk measure P. Notice that X**_{t}^{N}*(x)* is nothing but the linear

interpolation of*{x**Nt**/(σ*√

*N )*}*t**∈(NN)∩[0,1]**. For a= f, c we set*
*Q*^{a}_{N,ω}**:= P**^{a}*N,ω**◦ (X*^{N}*)*^{−1}*.*

*Then Q*^{a}_{N,ω}*is a measure on C([0, 1]), the space of continuous real-valued func-*
tions defined on the interval*[0, 1], and we want to study the behavior as N → ∞*
of this sequence of measures.

We start by fixing notation for the following standard processes:

*• the Brownian motion {B**τ*}*τ**∈[0,1]*;

*• the Brownian bridge {β**τ*}*τ**∈[0,1]*between 0 and 0;

*• the Brownian motion conditioned to stay nonnegative on [0, 1] or, more pre-*
cisely, the Brownian meander *{m**τ*}*τ**∈[0,1]* (cf. [26]) and its modification by a
random flip*{m*^{(p)}*τ* }*τ**∈[0,1]**, defined as m*^{(p)}*= σm, where P(σ = 1) = 1 − P(σ =*

*−1) = p ∈ [0, 1] and (m, σ) are independent;*

*• the Brownian bridge conditioned to stay nonnegative on [0, 1] or, more pre-*
cisely, the normalized Brownian excursion*{e**τ*}*τ**∈[0,1]*, also known as the Bessel
bridge of dimension 3 between 0 and 0; see [26]. For p*∈ [0, 1], {e**τ** ^{(p)}*}

*τ*

*∈[0,1]*is

*the flipped excursion defined in analogy with m*

*;*

^{(p)}*• the skew Brownian motion {B**τ** ^{(p)}*}

*τ*

*∈[0,1]*and the skew Brownian bridge

*{β*

*τ*

*}*

^{(p)}*τ*

*∈[0,1]*

*of parameter p (cf. [26]) the definitions of which are recalled in*Remark1.5below.

*We introduce a final process, labeled by two parameters p, q∈ [0, 1]: consider*
*a random variable U* *→ [0, 1] with the arcsine law P(U ≤ t) =* _{π}^{2}arcsin√

*t* and
*processes β*^{(p)}*, m*^{(q)}*as defined above, with (U, β*^{(p)}*, m*^{(q)}*)*an independent triple.

We then denote by*{B**τ** ^{(p,q)}*}

*τ*

*∈[0,1]*the process defined by

*B*

_{τ}*:=*

^{(p,q)}

√*U β*_{τ/U}^{(p)}*,* *if τ≤ U,*

√1*− Um*^{(q)}*(τ−U)/(1−U)**,* *if τ > U .*
(1.10)

We then have the following theorem, which is the main result of this paper (see Figure1).

THEOREM 1.3 (Scaling limits). *For every η∈ S, if N → ∞ along [N] = η,*
*then the sequence of measures{Q*^{a}_{N,ω}*} on C([0, 1]) converges weakly. More pre-*
*cisely:*

*(1) for δ*^{ω}*<1 (strictly delocalized regime), Q*^{c}_{N,ω}*converges to the law of e*^{(p}^{c}^{ω,η}^{)}*and Q*^{f}_{N,ω}*converges to the law of m*^{(p}^{f}^{ω,η}^{)}*for some parameters p*^{a}_{ω,η}*∈ [0, 1],*
*a= f, c;*

*(2) for δ*^{ω}*= 1 (critical regime), Q*^{c}_{N,ω}*converges to the law of β*^{(p}^{ω}^{)}*and Q*^{f}_{N,ω}*converges to the law of B*^{(p}^{ω}^{,q}^{ω,η}^{)}*for some parameters p**ω**, q**ω,η**∈ [0, 1];*

FIG. 1. *A schematic view of the scaling limits for the constrained endpoint case. While in the*
*localized regime (top image), on a large scale, the polymer cannot be distinguished from the interface,*
*in the strictly delocalized regime (bottom image) the visits to the interface are few and are all close*
*to the endpoints (the sign of the excursion is obtained by flipping one biased coin). In between, there*
*is the critical case: the zeros of the limiting process coincide with the zeros of a Brownian bridge, as*
*found for the homogeneous wetting case [6, 9, 17], but this time, the signs of the excursions vary and*
*they are chosen by flipping independent biased coin. Of course, this suggests that the trajectories in*
*the localized case should be analyzed without rescaling (this is done in [7]).*

*(3) for δ*^{ω}*>1 (localized regime), Q*^{a}_{N,ω}*converges, as N* *→ ∞, to the mea-*
*sure concentrated on the constant function taking the value zero (no need of the*
*restriction[N] = η).*

The exact values of the parameters p^{a}* _{ω,η}*, p

*ω*and q

*ω,η*are given in (5.5), (5.7), (5.17) and (5.19). See also Remarks5.3,5.4,5.7and5.8.

REMARK1.4. *It is natural to wonder why the results for δ** ^{ω}*≤ 1 may depend
on

*[N] ∈ S. First, we stress that only in very particular cases is there effectively a*

*dependence on η and we characterize these instances precisely; see Section*2.3. In particular, there is no dependence on

*[N] for the two motivating models (pinning*and copolymer) described in Section 1.1

*and, more generally, if h*

*ω*

*>*0. How-

*ever, this dependence on the boundary condition phenomenon is not a pathology,*

*but rather a sign of the presence of first-order phase transitions in this class of*models. Nonetheless, the phenomenon is somewhat surprising since the model is one-dimensional. This issue, which is naturally clarified when dealing with the infinite volume limit of the model, is treated in [7].

REMARK *1.5 (Skew Brownian motion).* *We recall that B*^{(p)}*(resp. β** ^{(p)}*) is a
process such that

*|B*

^{(p)}*| = |B| (resp. |β*

^{(p)}*| = |β|) in distribution, but in which*the sign of each excursion is chosen to be

*+1 (resp. −1) with probability p*(resp. 1

*− p) instead of 1/2. Observe that for p = 1, we have B*

^{(1)}*= |B|, β*

*=*

^{(1)}*|β|, m*^{(1)}*= m and e*^{(1)}*= e in distribution. Moreover, B*^{(1/2)}*= B and β*^{(1/2)}*= β in*
*distribution. Notice also that the process B*^{(p,q)}*differs from the p-skew Brownian*
*motion B** ^{(p)}*only for the last excursion in

*[0, 1], whose sign is +1 with probabil-*

*ity q instead of p.*

*1.5. Motivations and a survey of the literature. From an applied viewpoint, the*
interest in periodic models of the type we consider appears to be at least twofold:

(1) On one hand, periodic models are often (e.g., [13, 23]) motivated as carica-
*tures of the quenched disordered models, like those in which the charges are a*
typical realization of a sequence of independent random variables (e.g., [1, 4,
15, 28] and references therein). In this respect, periodic models may be viewed
*as weakly inhomogeneous, and the approximation of strongly inhomogeneous*
quenched models by periodic ones, in the limit of large period, gives rise to
very interesting and challenging questions. We believe that if the precise de-
scription of the periodic case that we have obtained in this work highlights
the limitations of periodic modeling for strongly inhomogeneous systems (cf.,
in particular, the anomalous decay of quenched partition functions along sub-
sequences pointed out in [16], Section 4 and our Theorem 1.2), it is at the
same time an essential step toward understanding the large period limit, and
the method we use in this paper may allow a generalization that yields infor-
mation on this limit.

(2) On the other hand, as mentioned above, periodic models are absolutely nat-
ural and of direct relevance in applications, for example, when dealing with
*molecularly engineered polymers; [24, 27] provide a sample of the theoretical*
physics literature, but the applied literature is extremely vast.

From a mathematical standpoint, our work may be viewed as a further step in the direction of:

(a) extending to the periodic setting precise path estimates obtained for homoge- neous models;

(b) clarifying the link between the free energy characterization and the path char- acterization of the different regimes.

With reference to (a), we point out the novelty with respect to the work on ho-
mogeneous models [6, 9, 17, 23]. Although the basic role of renewal theory tech-
niques in obtaining the crucial estimates has already been emphasized in [6, 9], we
stress that the underlying key renewal processes that appear in our inhomogeneous
*context are not standard renewals, but rather Markov renewal processes (cf. [2]).*

Understanding the algebraic structure leading to this type of renewal is one of the central points of our work; see Section3.1.

We also point out that the Markov renewal processes appearing in the criti-
*cal regime have step distributions with infinite mean. Even for ordinary renewal*
process, the exact asymptotic behavior of the Green function in the infinite mean
case has been a long-standing problem (cf. [14] and [20]) solved only recently
by Doney in [10]. The extension of this result to the framework of Markov re-
newal theory (that we consider here in the case of a finite-state modulating chain)
presents some additional problems (see Remark3.1and AppendixA) and, to our
knowledge, has not been considered in the literature. In Section5, we also give an
extension to our Markov renewal setting of the beautiful theory of convergence of
regenerative sets developed in [12].

A final observation is that, as in [6], the estimates we obtain here are really sharp in all regimes and our method goes well beyond the case of random walks with jumps±1 and 0, to which we restrict our attention for the sake of conciseness.

With reference to (b), we point out that in the models we consider, there is a va- riety of delocalized path behaviors which are not captured by the free energy. This is suggestive also in view of progressing in the understanding of the delocalized phase in the quenched models [16].

*1.6. Outline of the paper. The remainder of the paper is organized as follows.*

• In Section2, we define the basic parameter δ* ^{ω}* and analyze the dependence of
our results on the boundary condition

*[N] = η.*

• In Section3, we clarify the connection with Markov renewal theory and obtain
*the asymptotic behavior of Z*^{c}_{N,ω}*and Z*_{N,ω}^{f} , proving Theorem1.2.

• In Section4, we present a basic splitting of the polymer measure into zero level set and excursions and discuss the importance of the partition function.

• In Section5, we compute the scaling limits of P^{a}* _{N,ω}*, proving Theorem1.3.

• Finally, the appendices contain the proofs of some technical results.

**2. A closer look at the main results.**

*2.1. The order parameter δ*^{ω}*.* A remarkable feature of our results (see Theo-
rem1.2and Theorem1.3) is the fact that the properties of the polymer measure
*are essentially encoded in a single parameter δ*^{ω}*that can be regarded as the order*
*parameter of our models. This subsection is devoted to defining this parameter,*
but we first need some preliminary notation.

We start with the law of the first return to zero of the original walk:

*τ*1*:= inf{n > 0 : S**n**= 0},* *K(n) := P(τ*1

*= n).*

(2.1)

It is a classical result [11], Chapter XII.7 that

∃ lim_{n}_{→∞}*n*^{3/2}*K(n)=: c**K**∈ (0, ∞).*

(2.2)

*The key observation is that by the T -periodicity of the charges ω and by the defi-*
nition (1.5) of h*ω*, we can define an*S × S matrix **α,β* by means of the following
relation:

*n*_{2}

*n=n*1+1

*ω*^{(}_{n}^{−1)}*− ω**n*^{(}^{+1)}

*= −(n*2*− n*1*)h**ω**+ **[n*1*],[n*2]*.*
(2.3)

We have thus decomposed the above sum into a drift term and a fluctuating term,
*where the latter has the remarkable property of depending on n*_{1} *and n*_{2} only
through their equivalence classes *[n*1*] and [n*2] in S. We can now define three
basic objects:

*• for α, β ∈ S and ∈ N, we set*

^{ω}_{α,β}*()*:=

*ω*^{(0)}* _{β}* +

^{}

*ω*

^{(0)}

_{β}*− ω*

_{β}

^{(}

^{+1)}^{}

*,*

*if = 1, ∈ β − α,*

*ω*^{(0)}* _{β}* + log

^{}

^{1}

_{2}

^{}1

*+ exp(−h*

*ω*

*+*

*α,β*

*)*

^{ }

*,*

*if > 1, ∈ β − α,*

*0,* otherwise;
(2.4)

*• for x ∈ N, we introduce the S × S matrix M**α,β*^{ω}*(x)*defined by
*M*_{α,β}^{ω}*(x):= e*^{}^{ω}^{α,β}^{(x)}**K(x)1***(x**∈β−α)*;
(2.5)

*• summing the entries of M*^{ω}*over x, we obtain anS × S matrix that we call B** ^{ω}*:

*B*

_{α,β}*:=*

^{ω}^{}

*x*∈N

*M*_{α,β}^{ω}*(x).*

(2.6)

The meaning of these quantities will become clear in the next subsection. For now
*we stress that they are explicit functions of the charges ω and of the law of the*
*underlying random walk (to ease notation, the ω-dependence of these quantities*
will often be dropped).

*Observe that B**α,β* is a finite-dimensional matrix with positive entries, hence
the Perron–Frobenius theorem (see, e.g., [2]) implies that B*α,β* has a unique real
*positive eigenvalue (called the Perron–Frobenius eigenvalue) with the property*
that it is a simple root of the characteristic polynomial and that it coincides with
the spectral radius of the matrix. This is exactly our order parameter:

*δ*^{ω}*:= Perron–Frobenius eigenvalue of B*^{ω}*.*
(2.7)

*2.2. A random walk excursions viewpoint. In this subsection, we are going to*
see that the quantities defined in (2.4) and (2.5) emerge in a natural way from the
*algebraic structure of the constrained partition function Z*_{N,ω}^{c} . Let us reconsider
our Hamiltonian (1.3): its specificity is due to the fact that it can be decomposed in
*an efficient way by considering the return times to the origin of S. More precisely,*
we define

*τ*0*= 0,* *τ**j*+1*= inf{n > τ**j**: S**n*= 0}

*for j* *∈ N and we set ι**N* *= sup{k : τ**k**≤ N}. We also set T**j* *= τ**j* *− τ**j*−1 and of
course *{T**j*}*j**=1,2,...* * is, under P, an i.i.d. sequence. By conditioning on τ and in-*
tegrating on the up–down symmetry of the random walk excursions, one easily
obtains the following expression for the constrained partition function:

*Z*^{c}_{N,ω}**= E**

_{ι}_{N}

*j*=1

exp^{}^{ω}_{[τ}_{j−1}_{],[τ}_{j}_{]}*(τ**j**− τ**j*−1*)*^{}*; τ**ι*_{N}*= N*

(2.8)

=

*N*
*k*=1

*t*_{0}*,...,t** _{k}*∈N
0

*=:t*0

*<t*

_{1}

*<*

*···<t*

*k*

*:=N*

*k*
*j*=1

*M*_{[t}^{ω}_{j}_{−1}_{],[t}_{j}_{]}*(t**j**− t**j*−1*)*

where we have used the quantities introduced in (2.4) and (2.5). This formula
*shows in particular that the partition function Z*_{N,ω}^{c} is a function of the entries
*of M** ^{ω}*.

We stress that the algebraic form of (2.8) is of crucial importance. It will be analyzed in detail and exploited in Section 3and will be the key to the proof of Theorem1.2.

*2.3. The regime ω∈ P . In this subsection, we look more closely at the depen-*
dence of our main result (Theorem1.3) on the boundary condition*[N] = η. It is*
convenient to introduce the subsetP of charges defined by

P *:= {ω : δ*^{ω}*≤ 1, h**ω**= 0, ∃α, β : **α,β**= 0},*
(2.9)

*where we recall that h**ω**and **α,β* have been defined respectively in (1.5) and (2.3).

*The basic observation is that if ω /*∈ P , the constants p^{c}* _{ω,η}*, p

^{f}

*, p*

_{ω,η}*ω*and q

*ω,η*

*actually have no dependence on η and all take the same value, namely 1 if h**ω**>*0
*and 1/2 if h** _{ω}*= 0 (see Remarks5.3,5.4,5.7and5.8). The results in Theorem1.3

*for δ*

*≤ 1 may then be strengthened in the following way:*

^{ω}PROPOSITION 2.1. *If ω /∈ P , then the sequence of measures {Q*^{a}_{N,ω}*} on*
*C([0, 1]) converges weakly as N → ∞. In particular, setting p**ω**:= 1 if h**ω* *>*0
*and p**ω*:= ^{1}_{2} *if h**ω*= 0:

*(1) for δ*^{ω}*<1 (strictly delocalized regime), Q*^{f}_{N,ω}*converges to the law of m*^{(p}^{ω}^{)}*and Q*^{c}_{N,ω}*converges to the law of e*^{(p}^{ω}* ^{)}*;

*(2) for δ*^{ω}*= 1 (critical regime), Q*^{f}_{N,ω}*converges to the law of B*^{(p}^{ω}^{)}*and Q*^{c}_{N,ω}*converges to the law of β*^{(p}^{ω}* ^{)}*.

This stronger form of the scaling limits holds, in particular, for the two moti-
vating models of Section1.1, the pinning and the copolymer models, for which ω
*never belongs toP . This is clear for the pinning case, where, by definition, *≡ 0,
*while the copolymer model with h**ω* *= 0 always has δ*^{ω}*>*1, as we will prove in

AppendixB. However, we stress that there do exist charges ω (necessarily belong-
ing toP ) for which there is indeed a dependence on *[N] = η in the delocalized*
and critical scaling limits. This interesting phenomenon may be understood in sta-
tistical mechanics terms and is analyzed in detail in [7].

**3. Sharp asymptotic behavior for the partition function.** In this section,
*we are going to derive the precise asymptotic behavior of Z*_{N,ω}^{c} *and Z*_{N,ω}^{f} , in par-
ticular, proving Theorem1.2. The key observation is that the study of the partition
function for the models we are considering can be seen as part of the framework
*of the theory of Markov renewal processes; see [2], Chapter VII.4.*

*3.1. A link with Markov renewal theory. The starting point of our analysis*
is equation (2.8). Let us call a function*N × S × S (x, α, β) → F**α,β**(x)*≥ 0 a
*kernel. For fixed x∈ N, F***·,·***(x)*is anS × S matrix with nonnegative entries. Given
*two kernels F and G, we define their convolution F∗ G as the kernel defined by*

*(F∗G)**α,β**(x)*:= ^{}

*y*∈N

*γ*∈S

*F**α,γ**(y)G**γ ,β**(x−y) =*^{}

*y*∈N

*[F (y)·G(x −y)]**α,β**,*
(3.1)

where*· denotes matrix product. Then, since, by construction, M**α,β**(x)= 0 if [x] =*

*β− α, we can write (*2.8) in the following way:

*Z*_{N,ω}^{c} =

*N*
*k*=1

*t*_{1}*,...,t** _{k}*∈N

*0<t*

_{1}

*<*

*···<t*

*k*

*:=N*

*[M(t*1*)· . . . · M(N − t**k*−1*)*]*0,**[N]*

(3.2)

=^{}^{∞}

*k=1*

*[M** ^{∗k}*]

*[0],[N]*

*(N ),*

*where F*^{∗n}*denotes the n-fold convolution of a kernel F with itself (the n*= 0 case
is, by definition,*[F*^{∗0}]*α,β**(x)***:= 1***(β**=α)***1***(x**=0)*). In view of (3.2), we introduce the
kernel

Z*α,β**(n)*:=^{}^{∞}

*k*=1

*[M** ^{∗k}*]

*α,β*

*(n)*(3.3)

*so that Z*_{N,ω}^{c} = Z_{[0],[N]}*(N )* *and, more generally, Z*_{N}^{c}_{−k,θ}

*k**ω* = Z_{[k],[N]}*(N* *− k),*
*k≤ N, where we have introduced the shift operator for k ∈ N,*

*θ**k*:R^{S}→ R^{S}*,* *(θ**k**ζ )**β* *:= ζ*_{[k]+β}*,* *β∈ S.*

*Our goal is to determine the asymptotic behavior as N* → ∞ of the kernel
Z*α,β**(N )and hence of the partition function Z*_{N,ω}^{c} . To this end, we introduce an im-
*portant transformation of the kernel M that exploits the algebraic structure of (3.3):*

*we suppose that δ*^{ω}*≥ 1 (the case δ*^{ω}*<*1 requires a different procedure) and set for
*b*≥ 0 (cf. [2], Theorem 4.6)

*A*^{b}_{α,β}*(x):= M**α,β**(x)e*^{−bx}*.*

*Let us denote by (b) the Perron–Frobenius eigenvalue of the matrix*^{}_{x}*A*^{b}_{α,β}*(x).*

*As the entries of this matrix are analytic and nonincreasing functions of b, (b) is*
*analytic and nonincreasing too, hence strictly decreasing, because (0)= δ** ^{ω}*≥ 1

*and (∞) = 0. Therefore, there exists a single value*

^{F}

*ω*

*≥ 0 such that (*

^{F}

*ω*

*)*= 1 and we denote by

*{ζ*

*α*}

*α*,

*{ξ*

*α*}

*α*the Perron–Frobenius left and right eigenvectors of

*x**A*^{F}_{α,β}^{ω}*(x), chosen to have (strictly) positive components and normalized in such*
a way that^{}_{α}*ζ**α**ξ**α*= 1 (the remaining degree of freedom in the normalization is
immaterial). We then set

*α,β**(x):= M**α,β**(x)e*^{−}^{F}^{ω}^{x}*ξ**β*

*ξ**α*

(3.4)

and observe that we can rewrite (3.2) as
Z*α,β**(n)= exp(F**ω**n)ξ**α*

*ξ**β*

*U**α,β**(n)* *where U**α,β**(n)*:=^{}^{∞}

*k*=1

*[** ^{∗k}*]

*α,β*

*(n).*

(3.5)

*The kernel U*_{α,β}*(n)*has a basic probabilistic interpretation that we now describe.

Notice first that by construction, we have ^{}_{β,x}*α,β**(x)= 1, that is, is a semi-*
*Markov kernel (cf. [2]). We can then define a Markov chain{(J**k**, T**k**)*} on S × N
by

*P[(J**k*+1*, T**k*+1*)= (β, x)|(J**k**, T**k**)= (α, y)] = **α,β**(x)*
(3.6)

and we denote by P*α* the law of*{(J**k**, T**k**)} with starting point J*0*= α (the value*
*of T*0*plays no role). The probabilistic meaning of U*_{α,β}*(x)*is then given by

*U**α,β**(n)*=^{}^{∞}

*k*=1

P*α**(T*1*+ · · · + T**k**= n, J**k**= β).*

(3.7)

We point out that the process*{τ**k*}*k≥0**defined by τ*_{0}*:= 0 and τ**k**:= T*1*+· · ·+T**k*un-
der the lawP*α**is what is called a (discrete) Markov renewal process (cf. [2]). This*
provides a generalization of classical renewal processes since the increments*{T**k*}
are not i.i.d. but rather are governed by the process *{J**k*} in the way prescribed
by (3.6). The process*{J**k**} is called the modulating chain and it is indeed a genuine*
Markov chain on S, with transition kernel ^{}*x*∈N*α,β**(x), while in general, the*
process*{T**k**} is not a Markov chain. One can view τ = {τ**n*} as a (random) subset
ofN. More generally, it is convenient to introduce the subset

*τ** ^{β}*:=

^{}

*k**≥0:J**k**=β*

*{τ**k**},* *β∈ S,*
(3.8)

so that equation (3.7) can be rewritten as

*U*_{α,β}*(n)*= P*α**(n∈ τ*^{β}*).*

(3.9)

*This shows that the kernel U**α,β**(n)* is really an extension of the Green function
of a classical renewal process. In analogy with the classical case, the asymptotic

*behavior of U**α,β**(n)is sharply linked to the asymptotic behavior of the kernel ,*
*that is, of M. To this end, we notice that our setting is a heavy-tailed one. More*
*precisely, for every α, β*∈ S, by (2.2), (2.5) and (2.4), we have

*x*lim→∞

*[x]=β−α*

*x*^{3/2}*M**α,β**(x)*

(3.10)

*= L**α,β*:=

*c**K*1

2

1*+ exp( **α,β**)*^{}exp^{}*ω*^{(0)}_{β}^{}*,* *if h**ω*= 0,
*c**K*1

2exp^{}*ω*^{(0)}_{β}^{}*,* *if h**ω**>*0.

The rest of this section is devoted to determining the asymptotic behavior of
*U**α,β**(n)* and hence of Z*α,β**(n), in particular, proving Theorem* 1.2. For conve-
nience, we consider the three regimes separately.

*3.2. The localized regime (δ*^{ω}*>1). If δ*^{ω}*>*1 then necessarilyF_{ω}*>*0. Notice
that^{}_{x}*α,β**(x) >*0, so that in particular the modulating chain*{J**k*} is irreducible.

The unique invariant measure*{ν**α*}*α* is easily seen to be equal to*{ζ**α**ξ**α*}*α*.
*Let us compute the mean µ of the semi-Markov kernel :*

*µ*:= ^{}

*α,β*∈S

*x*∈N

*xν**α**α,β**(x)*= ^{}

*α,β*∈S

*x*∈N

*xe*^{−}^{F}^{ω}^{x}*ζ**α**M**α,β**(x)ξ**β*

= −*∂*

*∂b*

*b*=F_{ω}*∈ (0, ∞)*

(for the last equality, see, e.g., [5], Lemma 2.1). We can then apply the Markov renewal theorem (cf. [2], Theorem VII.4.3) which in our periodic setting gives

∃ lim_{x}_{→∞}

*[x]=β−α*

*U*_{α,β}*(x)= Tν**β*

*µ.*
(3.11)

By (3.5), we then obtain the desired asymptotic behavior:

Z*α,β**(x)∼ ξ**α**ζ**β*

*T*

*µexp(*F_{ω}*x),* *x→ ∞, [x] = β − α,*
(3.12)

*and for α= [0] and β = η, we have proven part (3) of Theorem*1.2, with C_{ω,η}* ^{>}* =

*ξ*0

*ζ*

*η*

*T /µ.*

*3.3. The critical case (δ*^{ω}*= 1). In this case,*^{F}*ω*= 0 and equation (3.4) reduces
to

_{α,β}*(x)= M**α,β**(x)ξ**β*

*ξ**α*

*.*
(3.13)

*The random set τ** ^{β}* introduced in (3.8) can be written as the union τ

*=*

^{β}*k≥0**{τ*_{k}^{β}*}, where the points {τ*_{k}* ^{β}*}

*k*≥0 are taken in increasing order, and we set

*T*_{k}^{β}*:= τ*_{k}^{β}_{+1}*− τ*_{k}^{β}*for k≥ 0. Notice that the increments {T*_{k}* ^{β}*} correspond to sums of
the variables

*{T*

*i*

*} between the visits of the chain {J*

*k*

*} to the state β: for instance,*we have

*T*_{1}^{β}*= T**κ*+1*+ · · · + T**,*

*κ:= inf{k ≥ 0 : J**k**= β}, := inf{k > κ : J**k**= β}.*

Equation (3.6) then yields that*{T*_{k}* ^{β}*}

*k*≥0is an independent sequence underP

*α*and

*that for k≥ 1, the variables T*

_{k}

^{β}*have the same distribution q*

^{β}*(n)*:= P

*α*

*(T*

_{1}

^{β}*= n)*

*that does not depend on α. In general, the variable T*

_{0}

*has a different law,*

^{β}*q*

^{(α;β)}*(n)*:= P

*α*

*(T*

_{0}

^{β}*= n).*

*These considerations yield the following crucial observation: for fixed α and β,*
the process*{τ*_{k}* ^{β}*}

*k*≥0underP

*α*is a (delayed) classical renewal process, with typical

*step distribution q*

^{β}*(·) and initial step distribution q*

^{(α}

^{;β)}*(·). By (3.9), U*

*α,β*

*(n)*is nothing but the Green function (or renewal mass function) of this process; more explicitly, we can write

*U**α,β**(x)*=

*q*^{(α}* ^{;β)}*∗

^{}

^{∞}

*n*=0

*(q*^{β}*)*^{∗n}

(3.14) *(x).*

*Of course, q*^{(α}^{;β)}*plays no role in the asymptotic behavior of U**α,β**(x). The key*
*point is rather the precise asymptotic behavior of q*^{β}*(x)as x→ ∞, x ∈ [0], which*
is given by

*q*^{β}*(x)*∼ *c**β*

*x*^{3/2}*where c** _{β}*:= 1

*ζ*

*β*

*ξ*

*β*

*α,γ*

*ζ*_{α}*L*_{α,γ}*ξ*_{γ}*>0,*
(3.15)

as is proved in detail in AppendixA. The asymptotic behavior of (3.14) then fol- lows by a result of Doney (cf. [10], Theorem B):

*U**α,β**(x)*∼ *T*^{2}
*2π c*_{β}

√1

*x,* *x→ ∞, [x] = β − α*
(3.16)

*(the factor T*^{2} is due to our periodic setting). Combining equations (3.5), (3.15)
and (3.16), we finally get the asymptotic behavior ofZ*α,β**(x):*

Z*α,β**(x)*∼*T*^{2}
*2π*

*ξ**α**ζ**β*

*γ ,γ*^{}*ζ**γ**L*_{γ ,γ}*ξ*_{γ}

√1

*x,* *x→ ∞, [x] = β − α.*

(3.17)

*Taking α= [0] and β = η, we have the proof of part (2) of Theorem*1.2.

REMARK 3.1. We point out that formula (3.15) is quite nontrivial. First, the
*asymptotic behavior x*^{−3/2}*of the law of the variables T*_{1}* ^{β}* is the same as that of

*the T*

*i*

*, although T*

_{1}

*is the sum of a random number of the nonindependent vari-*

^{β}*ables (T*

*i*

*). Second, the computation of the prefactor c*

*β*is by no means an obvious

*task (we stress that the precise value of c*

*is crucial in the proof of Proposition5.5 below).*

_{β}*3.4. The strictly delocalized case (δ*^{ω}*<1). We prove that the asymptotic be-*
havior ofZ*α,β**(x)when δ*^{ω}*<*1 is given by

Z*α,β**(x)*∼^{}*[(1 − B)*^{−1}*L(1− B)*^{−1}]*α,β*

1
*x*^{3/2}*,*
(3.18)

*x→ ∞, [x] = β − α,*
*where the matrices L and B have been defined in (3.10) and (2.6). In particular,*
*taking α= [0] and β = η, (*3.18) proves part (1) of Theorem1.2with

*C*_{ω,η}^{<}*:= [(1 − B)*^{−1}*L(1− B)*^{−1}]*0,η**.*

*To start with, it is easily checked by induction that for every n*∈ N,

*x∈N*

*[M** ^{∗n}*]

*α,β*

*(x)= [B*

*]*

^{n}*α,β*

*.*(3.19)

Next, we claim that, by (3.10), for every α, β∈ S,

∃ lim_{x}_{→∞}

*[x]=β−α*

*x*^{3/2}*[M** ^{∗k}*]

*α,β*

*(x)*=

*k*−1

*i*=0

*B*^{i}*· L · B*^{(k}^{−1)−i}^{ }_{α,β}*.*
(3.20)

*We proceed by induction on k. The k*= 1 case is just equation (3.10) and we have
that

*M*^{∗(n+1)}*(x)*=

*x/2*
*y*=1

*M(y)· M*^{∗n}*(x− y) + M(x − y) · M*^{∗n}*(y)*^{}

*(strictly speaking this formula is true only when x is even, however the odd x case*
is analogous). By the induction hypothesis, equation (3.20) holds for every k*≤ n*
and, in particular, this implies that*{x*^{3/2}*[M** ^{∗k}*]

*α,β*

*(x)}*

*x*∈N is a bounded sequence.

Therefore, we can apply the dominated convergence theorem and using (3.19), we get

∃ lim_{x}_{→∞}

*[x]=β−α*

*x*^{3/2}^{}*M*^{∗(n+1)}^{ }_{α,β}*(x)*

=^{}

*γ*

*B**α,γ*

*n−1*
*i=0*

*B*^{i}*· L · B*^{(n}^{−1)−i}^{ }_{γ ,β}*+ L**α,γ**[B** ^{∗n}*]

*γ ,β*

=

*n*
*i*=0

*[B*^{i}*· L · B** ^{n−i}*]

*α,β*

*.*

Our purpose is to apply the asymptotic result (3.20) to the terms of (3.5) and we need a bound to apply the dominated convergence theorem. We are going to show that

*x*^{3/2}*[M** ^{∗k}*]

*α,β*

*(x)≤ Ck*

^{3}

*[B*

*]*

^{k}*α,β*

(3.21)