Construction of an Edwards’ probability measure on
Abstract
In this article, we prove that the measures associated to the onedimensional Edwards’ model on the interval converge to a limit measure when goes to infinity, in the following sense: for all and for all events depending on the canonical process only up to time , .
Moreover, we prove that, if is Wiener measure, there exists a martingale such that , and we give an explicit expression for this martingale.
10.1214/10AOP540 \volume38 \issue6 2010 \firstpage2295 \lastpage2321 \newproclaimremarkRemark
Construction of an Edwards’ probability measure
A]\fnmsJoseph \snmNajnudel\correflabel=e1]joseph.najnudel@math.uzh.ch
class=AMS] \kwd60F99 \kwd60G30 \kwd60G44 \kwd60H10 \kwd60J65. Edwards’ model \kwdpolymer measure \kwdBrownian motion \kwdpenalization \kwdlocal time.
1 Introduction and statement of the main theorems
Edwards’ model is a model for polymers chains, which is defined by considering Brownian motion “penalized” by the “quantity” of its selfintersections (see also [4]). More precisely, for , and , let be Wiener measure on the space , and the corresponding canonical process. The dimensional Edwards’ model on is defined by the probability measure on such that, very informally,
(1) 
where is a strictly positive parameter, and is Dirac measure at zero.
(In this article, we always denote by the expectation of a random variable under the probability .)
Of course, (1) is not really the definition of a probability measure, since the integral with respect to Dirac measure is not well defined. However, it has been proven that one can define rigorously the measure for , by giving a meaning to (1) (for , the Brownian path has no selfintersection, so the measure has to be equal to ).
In particular, for , one has formally the equality
(2) 
where is the continuous family of local times of (which is almost surely well defined).
Therefore, one can take the following (rigorous) definition:
Under , the canonical process has a ballistic behavior; more precisely, Westwater (see [22]) has proven that for , the law of under tends to , where is Dirac measure at and is a universal constant (approximately equal to 1.1).
This result was improved in [18] (see also [17]), where van der Hofstad, den Hollander and König show that tends in law to a centered Gaussian variable, which has a variance equal to a universal constant (approximately equal to 0.4; in particular, smaller than one).
Moreover, in [19], the authors prove large deviation results for the variable under .
In dimension , the problem of the definition of Edwards’ model was solved by Varadhan (see [16, 8, 10]). In this case, it is possible to give a rigorous definition of , but this quantity appears to be equal to infinity. However, if one formally subtracts its expectation (i.e., one considers the quantity: ), one can define a finite random variable which has negative exponential moments of any order; therefore, if we replace by this random variable in (1), we obtain a rigorous definition of . Moreover, this probability is absolutely continuous with respect to Wiener measure.
In dimension 3 (the most difficult case), subtracting the expectation (this technique is also called “Varadhan renormalization”) is not sufficient to define Edwards’ model. However, by a long and difficult construction, Weswater (see [20, 21]) has proven that it is possible to define the probability ; this construction has been simplified by Bolthausen in [1] (at least if is small enough). Moreover, the measures are mutually singular, and singular with respect to Wiener measure.
The behavior of the canonical process under , as , is essentially unknown for and . One conjectures that the following convergence holds:
where depends only on and , and where is equal to for and approximately equal to for (see [17], Chapter 1).
At this point, we note that all the measures considered above are defined on finite interval trajectories [exactly, on ].
An interesting question is the following: is it possible to define Edwards’ model on trajectories indexed by ?
More precisely, if is Wiener measure on and the corresponding canonical process, is it possible to define a measure (for all ) such that, informally,
In this article, we give a positive answer to this question in dimension one. The construction of the corresponding measure is analogous to the construction given by Roynette, Vallois and Yor in their articles about penalisation (see [14, 11, 13, 12]).
More precisely, let us replace the notation by for the standard Wiener measure and the notation by for the canonical process. If is the natural filtration of , and if for all , the measure is defined by
where is the jointly continuous version of the local times of (almost surely well defined), the following theorem holds.
Theorem 1.1
For all , there exists a unique probability measure such that for all , and for all events ,
(3) 
Theorem 1.1 is the main result of our article.
Let us remark that if () and , then , since is, by definition, absolutely continuous with respect to . Hence, if Theorem 1.1 is assumed, is equal to zero.
Therefore, the restriction of to is absolutely continuous with respect to the restriction of to , and there exists a martingale such that, for all ,
In our proof of Theorem 1.1, we obtain an explicit formula for the martingale . However, we need to define other notation before giving this formula.
Let be the measure on , defined by , and let be the set of functions from to such that
equipped with the scalar product
The operator defined from to by
(4) 
is the infinitesimal generator of the process killed at rate at level , where is a Bessel process of dimension two; it is a Sturm–Liouville operator, and there exists an orthonormal basis of , consisting of eigenfunctions of , with the corresponding negative eigenvalues: where is in the interval .
Moreover, the functions are analytic and bounded (they tend to zero at infinity, faster than exponentially), and is strictly positive (these properties are quite classical, and they are essentially proven in [17], Chapters 2 and 3; see also [6]).
Now, for , let us denote by a process from to such that:

is a squared Bessel process of dimension zero, starting at .

is an independent squared Bessel process of dimension two.
Moreover, let be a continuous function with compact support from to , and let be a strictly positive real such that for all . We define the following quantities:
where is defined by , and
With this notation, we can state the following theorem, which gives an explicit formula for the martingale .
Theorem 1.2
For all and for all continuous and positive functions with compact support, the quantity is finite, different from zero, and does not depend on the choice of such that outside the interval ; therefore, we can write: . Moreover, for all , the density of the restriction of to , with respect to the restriction of to , is given by the equality
(5) 
where denotes the function [which depends on the trajectory ] such that for all .
The independence of with respect to (provided the support of is included in ) can be checked directly by using the fact that
(6) 
is a martingale, property which can be easily proven by using the differential equation satisfied by .
For , and , let us now define the following quantity:
(7) 
where denotes the density of the first hitting time of zero of a Brownian motion starting at (or equivalently, the density of the last hitting time of of a standard Bessel process of dimension 3), and is the bridge of a Bessel process of dimension 3 on , starting at and ending at .
To simplify the notation, we set
Moreover, let us consider, for , the function defined by
(8) 
for .
With this notation, Theorem 1.2 is a essentially a consequence of the two propositions stated below.
Proposition 1.3
When goes to infinity,
(9) 
Proposition 1.4
When goes to infinity,
(10) 
where is a universal constant (in particular, does not depend on and ).
Moreover, for all , and the constant is given by the formula
In the proof of these two propositions, we use essentially the same tools as in the papers by van der Hofstad, den Hollander and König. In particular, for , Propositions 1.3 and 1.4 are consequences of Proposition 1 of [18].
However, for a general function , it is not obvious that one can deduce directly our results from the material of [18] and [19], since for , one has to deal with the family of local times of the canonical process on the intervals , and as for , but also on the support of . Moreover, some typos in [18] make the argument as written incorrect. For this reason, we present a proof of this result in a different way than was done in [18].
The next sections of this article are organized as follows. In Section 2, we prove that Propositions 1.3 and 1.4 imply Theorems 1.1 and 1.2; in Section 3, we prove Proposition 1.3. The proof of Proposition 1.4 is split into two parts: the first one is given in Section 4; the second one, for which one needs some estimates of different quantities, is given in Section 6, after the proof of these estimates in Section 5. In Section 7, we make a conjecture on the behavior of the canonical process under the limit measure .
2 Proof of Theorems 1.1 and 1.2 by assuming Propositions 1.3 and 1.4
Let us begin to prove the following result, which is essentially a consequence of Brownian scaling.
Proposition 2.1
Therefore,
Now, let us set: . By Brownian scaling, and have the same law. Consequently,
where , defined by , has a support included in .
Therefore, Proposition 2.1 is proven if we show that .
Now, by change of variable and scaling property of squared Bessel processes,
(12)  
By replacing by , one obtains
(13) 
which proves Proposition 2.1.
At this point, we remark that does not depend on (as written in Theorem 1.2), since does not appear in the lefthand side of (11).
Now, let be in . One has, for all ,
where is the continuous family of local times of the process .
Therefore, for all ,
Under and conditionally on , is fixed and by Markov property, is a standard Brownian motion.
Hence, if we assume Propositions 1.3 and 1.4, we obtain, by using Proposition 2.1,
Moreover,
if is large enough. On the other hand,
and for large enough,
Now, for all and , is different from zero (as written in Theorem 1.2), since it is the integral of a strictly positive quantity. Therefore,
and for fixed and large enough
Consequently, for all and , by dominated convergence
Hence,
where is defined by (5):
3 Proof of Proposition 1.3
If is a continuous function from to with compact support included in , one has
(14)  
by scaling properties of Brownian motion.
Hence, the righthand side of (14) is decreasing with , which implies (for )
by using the equality
By dominated convergence, Proposition 1.3 is proven if we show that
(15) 
In order to estimate the lefthand side of (15), we need the following lemma.
Lemma 3.1
For every positive and measurable function on
where the law of the process is defined in the following way:

for , is a squared Bessel process of dimension zero, starting at ;

for , is an independent inhomogeneous Markov process, which has the same infinitesimal generator as a twodimensional squared Bessel process for and the same infinitesimal generator as a zerodimensional squared Bessel process for ;

for , has the same law as .
For , let be a standard Brownian motion, an independent Brownian motion starting at , and let us denote by the inverse local time of at level 0, and the first time when reaches zero.
By [7] and [3], for every process on the space , which is progressively measurable with respect to the filtration ,
(16) 
where is a process such that for and for .
By applying (16) to the process defined by , and by using Ray–Knight theorems, one obtains Lemma 3.1.
An immediate application of this lemma is the following equality:
(17)  
In order to majorize this expression, let us prove another result, which is also used in the proof of Proposition 1.4.
Lemma 3.2
For all , and for all measurable functions from to , the following equality holds:
(18) 
where is defined by (7).
In particular,
where
Moreover, is bounded by a universal constant and
The process is a local martingale with bracket given, for , by
Therefore,
where is a Brownian motion starting at . Moreover, since stays at zero when it hits 0, the hitting time of zero for is . Hence, the change of variable gives