Asymptotic results in solvable twocharge models
Abstract.
In [16], a solvable two charge ensemble of interacting charged particles on the real line in the presence of the harmonic oscillator potential is introduced. It can be seen as a special form of a grand canonical ensemble with the total charge being fixed and unit charge particles being random. Moreover, it serves as an interpolation between the Gaussian orthogonal and the Gaussian symplectic ensembles and maintains the Pfaffian structure of the eigenvalues. A similar solvable ensemble of charged particles on the unit circle was studied in [17, 10].
In this paper we explore the sharp asymptotic behavior of the number of unit charge particles on the line and on the circle, as the total charge goes to infinity. We establish extended central limit theorems, BerryEsseen estimates and precise moderate deviations using the machinery of the modGaussian convergence developed in [6, 15, 14, 4, 7]. Also a large deviation principle is derived using the GärtnerEllis theorem.
MSC 2010 subject classifications: Primary 60B20, 82B05, 15B52, 15A15; Secondary
33C45, 60G55, 60F05, 60F10, 62H10.
Keywords: Random matrices, twocharge ensembles, large deviation principle, moderate deviations, central limit theorem, BerryEsseen estimate, local limit theorem, Gaussian orthogonal ensemble, circular orthogonal ensemble.
1. Introduction
1.1. A general twocharge ensemble
We first present a general model of charged particles with charge ratio interacting via a logarithmic potential. Let be nonnegative integers such that The twocomponent loggas to be considered consists of particles with unit charge and particles with charge two, located either on the line or on the unit circle and interacting via the logarithmic potential
(1) 
where the charge one particles are located at and charge two particles at respectively.
The system may be in the presence of an external field, and the interaction energy between the charges and the field is
for some potential .
The total energy is then
The partition function of the system is
We study a special form of the grand canonical ensemble, where the number of charges and are random but they sum up to the total charge for some Moreover, the probability of the system having particles with unit charge and particles with charge two is equal to
where
and is a parameter, called fugacity, which determines the probability of the system having a particular population vector .
In what follows we distinguish two models of twocharge ensemble, on the real line and on the unit circle. For both models, we study the distribution of the number of charge one particles for the proper scaling of the parameter , when the total charge tends to infinity.
The structure of the paper goes as follows. In Subsections 1.1.1 and 1.1.2 we discuss separately twocharge models on the line and on the circle, respectively. Thereafter, in Subsection 1.2 we recall the definition of modGaussian convergence and the limiting theorems this convergence implies. In Section 2, the limiting results for the twocharge models on the line are stated for two different scaling of the fugacity parameter. In Section 3, we introduce the limiting theorems for the two charge ensemble on the circle.
A twocharge ensemble on the real line
The charged particle model on the line, was introduced in [16]. A generalization of these models to more than two types of charges recently has been studied in [12]. The motivation to study the two charged particle model on the line comes from its analogy with the real Ginibre ensemble (instead of complex conjugate pairs of eigenvalues, we have particles of charge on the real line). For more details on the complex and real eigenvalues of the real Ginibre ensembles and the existence of Pfaffian structure on the particles in these ensembles, see [13, 11, 2].
We study the two charge models on the line in the presence of the harmonic oscillator potential As a result, the case gives exactly the Gaussian orthogonal ensemble (). In contrast, as the above model corresponds to the Gaussian symplectic ensemble (). Therefore, the twocharge ensemble on the line can be interpreted as an interpolation between these two point processes.
We denote by the number of particles with unit charge with the parameter The aim is to investigate the limiting behavior of the sequence depending on the fugacity parameter
Typically, we will focus on two cases and for some The case is the analog of the real Ginibre ensemble, i.e. the charge 1 particles play the role of real eigenvalues in real Ginibre and the charge 2 particles the role of pairs of complex conjugate eigenvalues.
For the case
Rider, Sinclair and Xu, using Laplace method, provide asymptotic formulas for the quantities ([16]). From our side, we will show large deviation principle (LDP) for the sequence as well as modGaussian convergence for the normalized sequence Note that the central limit theorem (CLT) will be one of the consequences of the modGaussian convergence mentioned above.
The case has been studied in details in [16] and the limiting results such as CLT and LDP have been derived. We extend their results by proving modGaussian convergence for the sequence and deducing all its consequences such as precise moderate deviations and extended central limit theorem.
In order to derive these limiting results, we will use the following characterization of the partition function, shown in Theorem 2.1 in [16] and following from the solvability of the ensemble
where
with being the generalized th Laguerre polynomial with parameter defined as
and orthogonal with respect to the measure on with density .
Thus, the probability generating function of can be written
(2) 
and then the moment generating function of is given by
(3) 
A twocharge ensemble on the circle
This model represents the circular version of the above model and has been introduced and studied in [8, 9, 10, 17]. We have again particles with unit charge and particles with charge two that are located on the unit circle and interact logarithmically in the absence of an external field. This ensemble forms a Pfaffian point process that interpolates between the circular and symplectic ensembles.
We denote by the number of particles with unit charge when
with Following Section 6.7.1 of [10], the probability generating function of is
(4) 
Taking into account this polynomial structure, Forrester used an argument of Bender [1] to prove the local limit theorem. In [17], the central limit theorem has been derived for the sequence by a direct computation using the moment generating function,
(5) 
We will deduce the LDP for the sequence and the modGaussian convergence with its consequences for the sequence
1.2. The framework of modGaussian convergence
The framework of modGaussian convergence for a sequence of random variables has been developed by Delbaen, Féray, Jacod, Kowalski, Méliot and Nikeghbali in [6, 15, 14, 4, 7]. For , set
Definition 1.1.
Let be a sequence of real valued random variables, and be their moment generating functions, which we assume to all exist in a strip . We assume that there exists an analytic function not vanishing on the real part of , such that locally uniformly on ,
where is some sequence going to infinity. We then say that converges modGaussian on with parameters and limiting function .
To establish the modGaussian convergence in two charge models, the following theorem will be useful. It is a straightforward consequence of Theorem 8.2.1 in [6].
Theorem 1.2.
Let be a sequence of bounded random variables with nonnegative integer values, with mean variance and third cumulant . Suppose that the probability generating function of can be factorized as
where each is a polynomial with nonnegative coefficients and maximal degree not depending on . If
(6) 
then the sequence
converges in the modGaussian sense with limiting function and parameter . The convergence takes place of the whole complex plane.
Remark 1.3.
If , we can take .
Although modGaussian convergence entails much more information, for the aim of this paper, it will be an instrument to deduce asymptotic results, such as a central limit theorem, precise deviations etc. In what follows we summarize some of the limiting results that modGaussian convergence implies (see [6], [7] for the results below). In the remaining part of this subsection, is a sequence converging modGaussian on with parameters and limiting function .
Theorem 1.4 (Precise deviations at the scale , Theorem 4.2.1 in [6]).
For ,
and for ,
Theorem 1.5 (Central limit theorem at the scale , Theorem 4.3.1 in [6]).
For ,
Note that Theorem 1.5 immediately implies a central limit theorem for the rescaled sequence (taking ).
Under additional assumptions, it is possible to deduce the speed of convergence of the above mentioned CLT and a local limit theorem as well.
Definition 1.6.
Let be a sequence of real random variables, and a sequence growing to infinity. Consider the following assertions:

Fix and . There exists a zone , , such that, for all in this zone, if , then
for some positive constants and , that are independent of .

One has
If Conditions 1 and 2 are satisfied, we say that we have a zone of control with index .
Theorem 1.7 (Speed of convergence, Theorem 2.16 in [7]).
Let be a sequence converging in modGaussian sense with a zone of control of index . In addition, we assume . Then,
where is the Kolmogorov distance and
(7) 
Theorem 1.8 (Local limit theorem, see [3]).
Let and be a fixed interval, with . Assume that conditions (Z1) and (Z2) hold. Then for every exponent ,
In particular, assuming , with one obtains
2. The limiting results for the twocharge ensemble on the line
2.1. The fugacity parameter increasing with
In this subsection we establish various asymptotic results in the case The first theorem is the large deviation principle for the random sequence (as before is the number of particles of unit charge). We use the notation of Dembo and Zeitouni (see [5]). In particular, we denote the limiting cumulant generating function and its LegendreFenchel transform, by and , respectively.
Theorem 2.1 (Ldp).
The sequence of random variables satisfies the LDP in at speed with good rate function
(8) 
where and is such that .
Proof.
We derive the above LDP as a consequence of the GärtnerEllis theorem (see Theorem 2.3.6 in [5]). Therefore, we need to estimate the limiting behavior of the normalized cumulant generating function . We know that
(9) 
For this propose, we use the relation between Legendre and Hermite polynomials:
([18] formula 5.6.1) and the representation by means of the Fourier transform
to get
(10) 
With the change of variables , we obtain
Hence, since and
(11) 
where
(12) 
To apply the Laplace method, we split the integral in two parts:
with
Let be the positive solution of
The function is convex. Its minimum, reached at , is
A simple look shows that , so that if we set
(13) 
we get, for every ,
(14) 
Let us reparametrize this function in terms of . From (13)
(15) 
so that, if we denote we can write
where
It is clear that (resp. ) is a differentiable function of (resp. ) on its domain (resp. ). It is then enough to apply the GärtnerEllis theorem.
Moreover, since we see that
and the supremum is reached in so that
This function is strictly convex and attains its minimum at and therefore, the proof follows. ∎
In the following proposition, we describe the precise asymptotic behavior of the three first cumulants of that are latter used to derive the modGaussian convergence.
Proposition 2.2.
Let be the third cumulant of , then
(16)  
(17) 
Proof.
Set and, for , denote by
Theorem 2.3 (ModGaussian convergence).
As , the sequence converges modGaussian with parameters and the limiting function The convergence takes place on the whole complex plane.
Proof.
Since the support of the measure of orthogonality of the polynomials is , it is known (Theorem 6.73 in [18]) that the zeros of are positive and then
with for all . The latter, together with equation (2), implies that the probability generating function of can be written as
(19) 
that is, as a product of polynomials with positive coefficients of degree . To establish the modGaussian convergence, now it suffices to apply Theorem 1.2 and Proposition 2.2. ∎
Remark 2.4.
It could be possible to apply Remark 1.3 to this case, but we did not push the computation to that step, not to enlarge this paper.
Corollary 2.5.
Proof.
We have only to prove 2) and actually it suffices to show that the sequence converges modGaussian with index of control and a zone of control , where . Using (19), can be written as
where are independent and denotes the Bernoulli distribution with parameter Since are independent, by Theorems 9.1.6 and 9.1.7 in [6], we have the following bounds on the cumulants
for all . Then Lemma 4.2 in [7], implies that
(20) 
for all with and . ∎
2.2. The fugacity parameter
Theorem 2.6 (Ldp).
For , the sequence of random variables satisfies the LDP with rate and good rate function
This result was implicitly obtained in [16] p. 131, with a different proof.
Proof.
The Laplace transform of from Equation (3) is equal to
We use Perron’s formula (see Theorem 8.22.3 in [18] ) to characterize the asymptotic behavior of Laguerre polynomials
which holds uniformly in any compact set of . We derive
(21) 
uniformly in any compact set of . We conclude that
and we may apply the GärtnerEllis theorem, giving the LDP with rate function given by the Legendre dual of . ∎
Remark 2.7.
Since is the Cramér transform of the Poisson distribution, we could expect to have a modPoisson convergence (see [6] for the details on modPoisson convergence). We have indeed
but this convergence is valid only for Therefore, the assumptions required for the modPoisson convergence are not satisfied.
In contrast, the renormalized sequence converges modGaussian. Indeed, from (21), the cumulant generating series of can be written as
for all , which readily implies the following theorem.
Theorem 2.8 (ModGaussian convergence).
As , the sequence converges modGaussian with parameters and the limiting function The convergence takes place on the whole complex plane.
3. The limiting results for the two charge ensemble on the circle
Let us recall that for every .
Theorem 3.1 (Ldp).
The sequence of random variables satisfies the LDP in with speed and good rate function which is the Legendre dual of
The function is convex on , reaches its unique minimum at and satisfies
(22) 
It means that, when tends to infinity, tends to in probability and that
Proof.
We use the GärtnerEllis theorem. From (5), the moment generating function of the sequence can be written as
From the convergence of the Riemann sums we have
(23)  
The first derivative is clearly
(24) 
and all the remaining assertions are straightforward. ∎
In the following proposition, we limiting results on the three first cumulants of .
Proposition 3.2.
If is the third cumulant of , then
(25)  
(26) 
Proof.
These limits coincide with the results of Theorem 2.2 in [17], up to a change of notation since with and
We set