Wronskian structures of planar symplectic ensembles
Abstract.
We consider the eigenvalues of non-Hermitian random matrices in the symmetry class of the symplectic Ginibre ensemble, which are known to form a Pfaffian point process in the plane. It was recently discovered that the limiting correlation kernel of the symplectic Ginibre ensemble in the vicinity of the real line can be expressed in a unified form of a Wronskian. We derive scaling limits for variations of the symplectic Ginibre ensemble and obtain such Wronskian structures for the associated universality classes. These include almost-Hermitian bulk/edge scaling limits of the elliptic symplectic Ginibre ensemble and edge scaling limits of the symplectic Ginibre ensemble with boundary confinement. Our proofs follow from the generalised Christoffel-Darboux formula for the former and from the Laplace method for the latter. Based on such a unified integrable structure of Wronskian form, we also provide an intimate relation between the function in the argument of the Wronskian in the symplectic symmetry class and the kernel in the complex symmetry class which form determinantal point processes in the plane.
2020 Mathematics Subject Classification:
Primary 60B20; Secondary 33C451. Introduction
In the study of integrable models in random matrix theory, the underlying structure is one of the most important features that makes asymptotic analysis possible. We shall study such a structure for the planar symplectic ensemble whose joint probability distribution follows
(1.1) |
where is the partition function. Here is a suitable function satisfying the complex conjugation symmetry , called the external potential. For the special case when , the measure (1.1) describes the distribution of the eigenvalues of quaternionic Gaussian random matrices, also known as the symplectic Ginibre ensemble [37].
It is well known [18] that as the system tends to occupy a certain compact subset called the droplet. We shall study the local statistics of the ensemble (1.1) in the vicinity of the real line. For this purpose, we define the rescaled point process as
(1.2) |
where is an appropriate -dependent factor called the microscopic scale, cf. (2.1) and (2.20). Here is the angle of the outward normal direction at the boundary if and otherwise .
By definition, the -point correlation function is the normalised probability that of the eigenvalues lie in infinitesimal neighbourhoods of , i.e.
(1.3) |
see also (3.1) for a more standard definition. Recall that for a skew-symmetric matrix , its Pfaffian is given by
where is the symmetric group of order . Recently, it was shown in [5] that for the symplectic Ginibre ensemble, when and thus , the -point correlation function converges, as , locally uniformly to the limit
(1.4) |
where the pre-kernel is of the form
(1.5) |
Here, is the Wronskian, and
(1.6) |
(See [42] for a related result. We also refer to [49, 30] for the spectral radius of the symplectic Ginibre ensemble.)
We emphasise that the function is not necessarily unique. For instance, the variation leads to the same pre-kernel in (1.5) if and . Thus, instead of in (1.6), one may take
(1.7) |
Note in particular that the -point function is given by
(1.8) |
For the symplectic Ginibre ensemble, it follows from that the real bulk case corresponds to the regime , whereas the real edge case corresponds to the regime . We also remark that for the real bulk case, one can evaluate the integral in (1.5) in terms of the error function, which corresponds to the representation of the pre-kernel in [41, 9], see [5, Remark 2.3.(ii)] for further details.
Let us now define
(1.9) |
With the choice of (1.6), it is easy to see that the function in (1.9) evaluates to
(1.10) |
Here, one may notice that (1.10) corresponds to the limiting local correlation kernel of the complex Ginibre ensemble whose joint probability distribution is given by
(1.11) |
with , which forms a determinantal point process, see e.g. [32]. In other words, a properly defined limiting local correlation function of the ensemble (1.11) is given by
(1.12) |
In what follows, we call the ensemble (1.11) the complex counterpart to the symplectic ensemble (1.1).
The above discussions already lead us to the following natural questions.
- •
- •
The main purpose of this study is to examine these questions for the elliptic Ginibre ensemble in the almost-Hermitian regime and the Ginibre ensemble with boundary confinements.
2. Discussions of main results
Beyond the standard scaling limits (1.6) arising from the symplectic Ginibre ensemble, we aim to derive further universality classes whose complex counterparts have been well studied. In this section, we introduce our models and state the main results.
Until further notice, the microscopic scale in (1.2) is given by
(2.1) |
This specific choice of the rescaling comes from the fact that the macroscopic density of with respect to the area measure is see [18, 50].
2.1. Symplectic elliptic Ginibre ensemble in the almost-Hermitian regime
We first study the symplectic elliptic Ginibre ensemble, a one-parameter family of random matrices indexed by a non-Hermiticity parameter . This model describes an interpolation between the Gaussian symplectic ensemble and the symplectic Ginibre ensemble . Its eigenvalue statistics correspond to (1.1) with the potential
(2.2) |
As the terminology “elliptic” indicates, the associated droplet is given by the ellipse
(2.3) |
From the general universality principle of random matrix theory, it can be expected that for any fixed , the local statistics of the ensemble in the large system coincide with the one (1.6) obtained from the case . Based on the skew-orthogonal Hermite polynomials introduced by Kanzieper [41], such a statement was recently proved in [7] for and in [22] for general . (We mention that the local statistic at can be special for certain random matrix models [21, 23, 40, 8, 31].)
On the other hand, when as with an appropriate rate, the limiting correlation functions obtained from the double scaling limit are no longer described in terms of (1.6), and it is natural to expect a non-trivial transition between the scaling limits of non-Hermitian and Hermitian random matrices. In general, such a transition appears in the so-called almost-Hermitian regime (or weak non-Hermiticity) introduced in the series of works [36, 34, 35] by Fyodorov, Khoruzhenko, and Sommers.

In Theorems 2.1 and 2.2 below, we state the scaling limits of the symplectic elliptic Ginibre ensembles in the almost-Hermitian regime, see Figure 1. We emphasise that for the special cases and , (cf. (2.3)) the scaling limits were studied in [41] and [10] respectively, based on the idea of Riemann sum approximation of skew-orthogonal Hermite polynomial kernels. This method is very useful in finding an explicit formula of the limiting pre-kernel, but it is not easy to perform the asymptotic analysis for general or to precisely control the error term.
Instead, we exploit the generalised Christoffel-Darboux formula introduced in [22], which allows us to obtain unified proofs for any points on the real line and to perform precise asymptotic analysis. (Indeed, this method can also be used to derive the subleading correction terms as well, see [22].) Furthermore, we describe the scaling limits in terms of the unified Wronskian form (1.5) and show that the associated kernels of the form (1.9) correspond to those obtained from their complex counterparts. These provide affirmative answers to the two questions in the previous section for the almost-Hermitian symplectic ensembles.
Our first main result is on the bulk scaling limit of the symplectic elliptic Ginibre ensemble in the almost-Hermitian regime.
Theorem 2.1.
With the choice (2.5), the pre-kernel has an alternative representation
(2.7) |
see Subsection 4.2. For , this form of the limiting pre-kernel was investigated in [41]. We emphasise that by means of Ward’s equation, it was shown in [5, Theorem 2.10] that the pre-kernel of the form (2.7) is a unique translation invariant scaling limit of general planar symplectic ensembles.
Note in particular that with (2.5), the kernel in (1.9) is given by
(2.8) |
The kernel (2.8) corresponds to the one obtained from the complex elliptic Ginibre, see e.g. [34, 6]. As one may notice, the factor in (2.6) corresponds to the density of the semi-circle law. We refer to [11] for the geometric interpretation of such a density term, which follows from the limiting shape of the droplet (also called cross-section convergence).
It is also easy to show that the function in (2.5) satisfies
(2.9) |
Thus one can recover (1.6) (cf. (1.7)) for the bulk case in the non-Hermitian limit when .



We now turn to the edge scaling limit.
Theorem 2.2.
Notice that with (2.11), the kernel in (1.9) is given by
(2.12) |
Again, the kernel (2.12) corresponds to the complex counterpart obtained first in [19, 3]. (We also refer the reader to [11, 10] for alternative derivations of (2.12).)
We now briefly discuss the non-Hermitian limit when . It follows from the asymptotic behaviour of the Airy function (see e.g. [47, Eq.(9.7.5)])
(2.13) |
that the function in (2.11) satisfies
(2.14) |
Thus one can recover (1.6) (cf. (1.7)) for the edge case as well.



2.2. Symplectic Ginibre ensemble with boundary confinements
In the previous subsection we have discussed the ensembles (1.1) whose potential does not have any constraints near the boundary. In what follows, we call such a situation as free boundary (or soft edge) condition. On the other hand different universality classes naturally arise at the edge, if appropriate boundary constraints are imposed. Typical examples of such boundary conditions are the so-called soft/hard edge and hard edge constraints.
In the soft/hard edge setting, we completely confine the particles inside of the droplet by redefining the potential outside . This type of boundary confinement does not change the limiting spectral distribution. The term soft/hard comes from this situation being called when “the soft edge meets the hard edge” [27]. For the complex ensembles (1.11), such a boundary condition has been investigated in [15, 16, 20, 51] for example.
In the hard edge setting, we confine the particles further inside of the droplet . This leads to a modified associated equilibrium measure, in particular giving rise to some non-trivial measure on a certain one-dimensional subset. From a statistical physics point of view, this confinement has the effect of condensing a non-trivial portion of the particles onto the hard edge. We refer to [52, 38, 46] and references therein for the studies of complex ensembles (1.11) with such type of boundary confinement. (See also [57] for a similar situation in the context of truncated unitary ensembles.)
To our knowledge, the edge scaling limits of the symplectic ensembles associated with the above boundary conditions have not been investigated, and we aim to contribute to these problems. In particular, in Theorems 2.3 and 2.4, we derive the scaling limits of the symplectic Ginibre ensembles with soft/hard edge and hard edge constraints, which provide new universality classes. See Figure 4 for the graphs of the corresponding one-point functions. Furthermore, we shall show that the limiting pre-kernels are again of Wronskian form and that the relation to their complex counterparts again holds.
First, we consider the soft/hard edge Ginibre ensemble, which corresponds to the configuration (1.1) with the potential
(2.15) |
By construction, all the eigenvalues are completely confined inside the droplet . As a result, the rescaled point process (1.2) at the real edge of the spectrum lies only in the left-half plane . Since the limiting spectral distribution (the circular law) is the same as the usual symplectic Ginibre ensemble, we rescale the process with the choice of microscopic scale (2.1). We then obtain the following.
Theorem 2.3.
Note that with (2.16), the kernel in (1.9) is given by
(2.17) |
Again, (2.17) corresponds to the kernel of the complex Ginibre point process with soft/hard edge condition, see e.g. [14, 15, 32].
We now turn to the hard edge Ginibre ensemble. This corresponds to the ensemble (1.1) with the potential
(2.18) |
By definition, the eigenvalues are confined in a disk . Thus the rescaled processes are again confined in . In this case, the associated equilibrium measure is no longer absolutely continuous with respect to the area measure and rather it is of the form
(2.19) |
where is the normalized arc-length measure on . Therefore we choose the micro-scale at the edge-point as
(2.20) |
We then obtain the following.
Theorem 2.4.
(Non-Hermitian hard edge scaling limit) Let be the hard edge Ginibre potential (2.18). Then for , the -point correlation function converges, as , locally uniformly to the limit
(2.21) |
where the pre-kernel is of Wronskian form
(2.22) |
We mention that the function can be written in terms of the incomplete gamma function as
(2.23) |
Compared to the limiting correlation functions of the form (1.4) with (1.5), in the hard edge scaling limit (2.21), there are no Gaussian factors. This comes from the micro-scale (2.20) used only for the hard edge ensemble among the models under consideration in the present work. By a similar reason, with the choice of (2.22), it is natural to consider
(2.24) |
rather than the one of the form (1.9). Then this kernel (2.24) corresponds to the one obtained from the complex Ginibre ensemble with hard edge constraint, see [52].
Concerning the equivalence of the truncated unitary and hard edge Ginibre ensemble [52, 57], we expect that the scaling limit (2.22) coincides with the one obtained in the context of the truncated symplectic ensemble, see [45, 43] for the scaling limit away from the real edge.



Remark (Numerics on subleading terms).
Beyond the limiting correlation functions, a natural question arising in the study of scaling limits is their rates of convergence as . For instance, these were obtained in [22] for the symplectic elliptic Ginibre ensemble with fixed . (See also [33] for the Hermitian counterparts.) In particular, it was shown that in the edge scaling limit, the convergence rate is of order .
The precise asymptotic expansions in the situations of Theorems 2.2, 2.3, and 2.4 exceed the scope of this paper. Nevertheless, we present some relevant numerical simulations. In particular, from the numerics below, we observe that the rates of convergences are of order in Theorem 2.2 (cf. [33]), of order in Theorem 2.3 and of order in Theorem 2.4.






We end this section by giving a remark on universality.
Remark (Towards local universality).
Let Then integration by parts gives rise to
(2.25) |
For the free boundary cases, the first term on the right-hand side corresponds to the kernel of the complex ensembles, cf. (1.9).
The finite- version of this equation for the (elliptic) Ginibre potential was introduced in [5, 22] as a version of the Christoffel-Darboux formula. Such a relation for some singular potentials has been investigated as well, which requires higher (or fractional) order differential operators, see [2, 48, 4] for the Laguerre ensembles and [5, 17, 26] for the Mittag-Leffler ensembles.
We also refer to [1, 56] for similar equations in the Hermitian random matrix theory. Together with the local universality of the complex ensembles, this relation plays an important role in the study of local universality for symplectic ensembles [28, 29]. In a similar spirit, we expect that the finite- version of the identity (2.25) together with the bulk/edge universality of the determinantal Coulomb gas [13, 39] (cf.[12]) provides key ingredients in universality problems for planar symplectic ensembles.
Organisation of the paper.
In Section 3, we compile and summarise the relevant materials on the planar symplectic ensembles such as the skew-orthogonal polynomial representation of the pre-kernel.
3. Preliminaries
By definition, the -point correlation function of the system (1.1) is given by
(3.1) |
It is well known [41] that the ensemble (1.1) forms a Pfaffian point process. In other words, there is a two-variable function , called the (skew) pre-kernel, such that
(3.2) |
By a change of measures, it is easy to see that the correlation function of the rescaled process in (1.2) is given by
(3.3) |
In particular, with the rescaled pre-kernel
(3.4) |
we have
(3.5) |
We remark that different pre-kernels may give rise to the same correlation functions. In particular, we call two pre-kernels and equivalent if there exists a sequence of unimodular functions with such that . In what follows, we also call a cocycle.
The skew-symmetric form is given by
Let be a family of monic polynomials of degree that satisfy the following skew-orthogonality conditions with skew-norms : for all
(3.6) |
where is the Kronecker delta. Then the pre-kernel has a canonical representation in terms of the skew-orthogonal polynomials
(3.7) |
We present some examples of skew-orthogonal polynomials that will be used in the following sections.
-
•
Example 1. (Elliptic potential) For the elliptic potential in (2.2), it was obtained by Kanzieper [41] that the associated skew-orthogonal polynomials can be expressed in terms of the Hermite polynomial
(3.8) To be precise, we have
(3.9) and their skew-norms are given by
(3.10) This also follows from a more general method of constructing skew-orthogonal polynomials [7, Theorem 3.1].
- •
4. Scaling limits of the almost-Hermitian ensembles
In this section, we study the elliptic Ginibre ensemble in the almost-Hermitian regime and prove Theorems 2.1 and 2.2. In Subsection 4.1, we outline the strategy of our proofs based on the Christoffel-Darboux formula. Subsections 4.2 and 4.3 are devoted to the study of the bulk scaling limit (Theorem 2.1) and the edge scaling limit (Theorem 2.2) respectively.
4.1. Strategy of the proof: the Christoffel-Darboux formula
Combining (3.4), (3.7), (3.9), (3.10), we have the canonical representation of the pre-kernel
Following [22], let us introduce
(4.1) |
where
(4.2) |
Note that by (2.2) and (2.1), we have
(4.3) |
where the cocycle is given by
(4.4) |
Therefore as , we have the uniform limit
(4.5) |
where . Here the convergence is uniform on compact subsets of .
The key idea which allows us to perform the asymptotic analysis is the following version of the Christoffel-Darboux formula [22, Proposition 1.1].
Lemma 4.1.
(Christoffel-Darboux formula for the skew-orthogonal Hermite polynomial kernel) We have
(4.6) |
where
(4.7) |
and
(4.8) |
We now write
(4.9) |
and
(4.10) |
where the convergence is uniform on compact subsets of (we will show this later in the proof of Proposition 4.4). Then by Lemma 4.1, we have
(4.11) |
We analyse the large- limit of and using the expressions
(4.12) |
The following version of the Christoffel-Darboux formula for the kernel of the complex elliptic Ginibre ensemble was obtained by Lee and Riser [44, Proposition 2.3], see also [25, Section 3] for more general identities of such kind.
Lemma 4.2.
(Christoffel-Darboux formula for the orthogonal Hermite polynomial kernel) The function
(4.13) |
satisfies
(4.14) |
Using this lemma, we have the following expressions.
Lemma 4.3.
We have
(4.15) |
and
(4.16) |
where
(4.17) |
4.2. Almost-Hermitian bulk scaling limit
In this subsection, we consider the almost-Hermitian bulk scaling limit where is given by (2.4) and .
We aim to show the following proposition.
Proposition 4.4.
Proof of Theorem 2.1.
The rest of this subsection is devoted to the proof of Proposition 4.4. In the sequel, we shall consider the case as the other case follows along the same lines with slight modifications. For the asymptotic analysis, we shall use the following strong asymptotics of the Hermite polynomials.
Lemma 4.5.
Proof.
Lemma 4.6.
As , we have
(4.22) |
and
(4.23) |
Proof.
We first consider the case (note that ). In this case, it follows from Lemma 4.5 that
(4.24) |
Also notice that
(4.25) |
Then by Lemma 4.3, we obtain
(4.26) |
and
(4.27) |
Next, we prove the lemma for the case . By Lemma 4.5 and the elementary identity of trigonometric functions, we have
Then it follows from
that
Notice here that we have
Now Lemma 4.3 completes the proof.
∎
Lemma 4.7.
The function I given by (4.18) satisfies
(4.28) |
Proof.
By definition, we have
Now the lemma follows from integration by parts. ∎
We are now ready to prove Proposition 4.4.
Proof of Proposition 4.4.
For the function I, we choose the initial value As discussed in [22], this corresponds to the microscopic density of the complex elliptic Ginibre ensemble. Thus the initial value follows from for instance [34, 6, 11].
Now it remains to show that for some choice of . Here we choose .
Let us write
(4.29) |
where
(4.30) |
We claim that . Then it follows from straightforward computations using Lemma 4.5 that
To analyse the term , let us write
(4.31) |
Due to the Hermite numbers , we have by Taylor expansion
(4.32) |
By applying Lemmas 4.2 and 4.5,
(4.33) |
Invoking this asymptotics, the resulting oscillatory integral in (4.31) can be analysed in the same way as [24, Lemma 4.4]. To be more precise, it has the asymptotic form , where
(4.34) |
and
(4.35) |
Since as , the Riemann–Lebesgue lemma yields
see [24, Lemma 4.4] for further details. This completes the proof.
∎
4.3. Almost-Hermitian edge scaling limit
In this subsection, we consider the almost-Hermitian edge scaling limit where is given by (2.10) and . As in the previous subsection, we need to show the following.
Proposition 4.8.
We have
(4.36) |
and
(4.37) |
We remark that by (2.13), it is easy to see that
(4.38) |
These terms appear in the limiting differential equations for the non-Hermitian regime, see [5, 22] .
Proof of Theorem 2.2.
Let us write
(4.39) |
where and are given by (2.11). Then similarly as above, it suffices to show that the function is a unique anti-symmetric solution, which satisfies (4.11) with (4.36) and (4.37).
To see this, note that
(4.40) |
This gives
(4.41) |
Notice that since
we have
(4.42) |
Therefore we obtain
which completes the proof. ∎
The rest of this subsection is devoted to the proof of Proposition 4.8.
For the asymptotic analysis, we shall use the following (critical) strong asymptotics of the Hermite polynomials.
Lemma 4.9.
Proof.
Lemma 4.10.
As , we have
(4.46) |
and
(4.47) |
Proof.
First note that for given in (4.17) we have
(4.48) |
We also have
(4.49) |
and
(4.50) |
These give rise to
(4.51) |
Proof.
The second identity is obvious since
For the first one, note that
Thus we have
Therefore all we need to show is
(4.57) |
This follows from straightforward computations using integration by parts. ∎
We now prove Proposition 4.8.
Proof of Proposition 4.8.
As above, for the function I, we choose the initial value . Then the value of follows from [19, 3, 11].
Therefore again, it remains to show for some . For , we shall show that
(4.58) |
For this purpose, we again use the expression (4.29). After inserting (2.10), the limit of the prefactor is easy to compute and for the Hermite polynomials in we use the asymptotics from Lemma 4.9. Only the sum with remains for which we use the following ansatz:
(4.59) |
where . Note that by Lemma 4.2, we have
(4.60) |
Recall also that by (4.32), we have .
In the bulk we use the Hermite asymptotics from the oscillatory regime
(4.61) |
where the -term is uniform because , cf. [53, Eq. (3.3), (3.5)]. Hence the derivative has the asymptotic form
(4.62) |
Thus we conclude that there exists a constant such that for all sufficiently large and all the following inequality holds
(4.63) |
Since we obtain for .
Near the edge we set . Then by Lemma 4.9 we have
(4.64) |
and the error term is again uniform because , cf. [53, Eq. (3.11), (3.13)]. Thus we obtain that
(4.65) |
Also note that the Airy-function has the following Laplace transform [47, Eq. (9.10.13)]
(4.66) |
Therefore we can rewrite the initial value as an integral over with . Combining all of the above with (4.59), we arrive at
(4.67) |
Now, it follows from the straightforward computations of the pre-factors that the claimed formula (4.58) holds.
∎
5. Scaling limits of the soft/hard and hard edge ensembles
In this section, we study the Ginibre ensemble with boundary confinements and prove Theorems 2.3 and 2.4. In the first subsection, we summarise the strategy of our proofs using the Laplace method. In Subsections 5.2 and 5.3, we derive the boundary scaling limits of the Ginibre ensemble with soft/hard edge (Theorem 2.3) and hard edge (Theorem 2.4) constraint respectively.
5.1. Strategy of the proof: the Laplace method
We consider the potential of the form (2.15) and (2.18). Let us write
(5.1) |
for the weighted orthonormal polynomial, where is the orthogonal norm given by (3.11). Since is radially symmetric, it follows from (3.12), (3.4) and (3.7) that the rescaled and weighted pre-kernel can be written as
(5.2) |
where
(5.3) |
Here and in the sequel, we denote and , where is the microscopic scale given by (2.1) for the soft/hard edge case and by (2.20) for the hard edge case. Throughout this section, we write
(5.4) |
Recall also that .
The general strategy of deriving the large- behaviour of is as follows:
-
•
for , we compute the asymptotic behaviours of by means of Laplace’s method;
-
•
we show that in the case of soft/hard edge ensemble, the dominant terms of near consist of with , whereas in the case of hard edge ensemble, those near consist of with ;
-
•
by discarding the lower degree terms, we compile the contributions of dominant terms using the Riemann sum approximation.
We now present some asymptotic behaviours of . For this purpose, we define
(5.5) |
Observe that the function has a unique critical point at . It is convenient to write
(5.6) |
We first obtain the following. (See [52, Lemmas 3.1, 3.3]) for a related statement.)
Lemma 5.1.
For a given , let . Then we have the following.
-
(i)
For each with , we have
(5.7) where uniformly for as .
-
(ii)
For each with , we have
(5.8) where uniformly for as .
Proof.
For with , the orthogonal norm can be written as
(5.9) |
where . By the Taylor expansion
(5.10) |
at the critical point , we obtain that as ,
(5.11) |
Here, -constant can be taken independent of . Note that by (5.4),
Therefore, by the change of variable , we have
(5.12) |
as . Since , it follows from (5.11) and (5.12) that
(5.13) |
where uniformly for as .
On the other hand, the second integral on the right-hand side of (5.9) is negligible. To see this, observe first that and for . Thus for
for some positive constant . This gives that as
(5.14) |
We have shown the first assertion of the lemma.
Next, we show the second assertion. The proof is similar to that of Lemma 5.1 (i). A key observation is that for with
which completes the proof. ∎
5.2. Non-Hermitian soft/hard edge scaling limit
In this subsection, we prove Theorem 2.3. We first show the following proposition (here denotes the left half plane).
Proposition 5.2.
There exists a sequence of cocycles such that
where the convergence is uniform for in compact subsets of . Here, the function is given by (5.6).
Proof of Theorem 2.3. .
Lemma 5.3.
For with we have
(5.15) |
where . Here, uniformly for all in any compact set as .
Proof.
Lemma 5.4.
Let be a compact subset of . Then there exist positive constants and such that for all with and for all ,
(5.16) |
Proof.
For , the critical point of satisfies
(5.17) |
Choose a constant such that . Since for , we obtain
(5.18) |
for some constant . By (5.1), this gives that for ,
for some constants and . ∎
Lemma 5.5.
Let be a compact subset of . Then for , we have
(5.19) |
Proof.
We first mention that the number of summands as . For each , the norm can be expressed in terms of the lower incomplete function as
It is well known that if is a random variable, then
For all , the normal approximation of Poisson random variables gives
where is a standard normal random variable. Note that for some constant . This implies that there exists a constant such that
(5.20) |
where uniformly for as . This implies that and for
(5.21) |
Combining all of the above with Lemma 5.4, we obtain
(5.22) |
uniformly for . Similarly, we obtain
(5.23) |
which completes the proof. ∎
We now show the following.
Lemma 5.6.
For with , we have
(5.24) |
Proof.
We are now ready to show Proposition 5.2.
5.3. Non-Hermitian hard edge scaling limit
This subsection is devoted to the proof of Theorem 2.4. As in the previous subsection, by (5.2), it is enough to show the following proposition.
Proposition 5.7.
There exists a sequence of cocycles such that
where the convergence is uniform for in any compact subset of .
We first show the following lemma.
Lemma 5.8.
For each with and for all in a compact set, we have
where is a unimodular function.
Proof.
Lemma 5.9.
Let be a compact subset of . Then there exists a positive constant such that for all with and for all ,
Proof.
By Lemma 5.1 (i), we have
(5.29) |
where and is the critical point of . The Taylor expansion of at gives
(5.30) |
since as . Here uniformly for . Note that . By (5.6), it follows from the well-known asymptotics of the complementary error function (see e.g. [47, Section 7.12]) that
This completes the proof. ∎
As in Section 5.2, the following lemma asserts that the weighted polynomials of lower degree tend to exponentially decay in a neighbourhood of the hard edge. For the proof of the lemma, we refer to Lemma 3.5 in [52].
Lemma 5.10.
Let be a compact subset of . Then for with and for , we have
(5.31) |
where and are positive constants.
As a counterpart of Lemma 5.5, we obtain the following lemma which allows us to discard the lower degree polynomials in the sum (5.3).
Lemma 5.11.
Let be a compact subset of . Then for , we have
(5.32) |
Proof.
Similarly as in the proof of Lemma 5.5, we obtain for all with
(5.33) |
for some constant . Together with Lemma 5.10, this implies for all
(5.34) |
since all the weighted polynomials and in the sum are exponentially small as .
Now for with , it follows from Lemma 5.1 (i) (see also Lemma 5.6) that
(5.35) |
This gives the estimate for with and
Note that Lemma 5.9 gives a bound for all with and all . Since the sum
(5.36) |
contains terms and , the sum (5.36) has a bound of .
Finally, we obtain that
(5.37) |
since is at most for , for , and with is exponentially small. Hence, the proof is complete. ∎
Lemma 5.12.
For , with and , we have
(5.38) |
where as .
Proof.
We now prove Proposition 5.7.
Acknowledgement.
We wish to express our gratitude to Gernot Akemann, Nam-Gyu Kang, and Iván Parra for helpful discussions.
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