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A Riemann Hilbert Approach to the Study of the Generating Function Associated to the Pearcey Process

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Abstract

Using Riemann–Hilbert methods, we establish a Tracy–Widom like formula for the generating function of the occupancy numbers of the Pearcey process. This formula is linked to a coupled vector differential equation of order three. We also obtain a non linear coupled heat equation. Combining these two equations we obtain a PDE for the logarithm of the the generating function of the Pearcey process.

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Acknowledgements

I am very grateful to Mattia Cafasso for many advices given and his support during the preparation of this paper. This work has been supported by the European Union Horizon 2020 research and innovation program under the Marie Sklodowska-Curie RISE 2017 Grant Agreement No. 778010 IPaDEGAN and by the IRP Probabilités Intégrables, Intégrabilité Classique et Quantique (PIICQ), funded by the CNRS.

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Correspondence to Thomas Chouteau.

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The authors have no competing interests to declare that are relevant to the content of this article. There are no financial and non-financial conflicts of interest.

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Communicated by Alexander P. Veselov.

Appendix

Appendix

1.1 Asymptotics of P and Q

As \(s\rightarrow \infty \), P and Q have the following asymptotic.

Proposition 5.1

Let P and Q defined respectively as in Eqs. (4.7) and (4.6). Then, as \(s\rightarrow \infty \):

$$\begin{aligned}{} & {} Q(s)\displaystyle \sim _{s\rightarrow \infty }\sqrt{\dfrac{2}{3\pi }}s^{-1/3}e^{-\frac{3}{8}s^{4/3}-\frac{\tau }{4}s^{2/3}+\frac{\tau ^2}{6}}\cos \left( \frac{3}{4}\sin \left( \frac{2\pi }{3}\right) s^{4/3}-\frac{\tau }{2}\sin \left( \frac{2\pi }{3}\right) s^{2/3}-\frac{\pi }{6}\right) \nonumber \\ \end{aligned}$$
(5.1)
$$\begin{aligned}{} & {} P(s)\displaystyle \sim _{s\rightarrow \infty }\sqrt{\dfrac{2}{3\pi }}s^{-1/3}e^{\frac{3}{8}s^{4/3}+\frac{\tau }{4}s^{2/3}-\frac{\tau ^2}{6}}\cos \left( \frac{3}{4}\sin \left( \frac{2\pi }{3}\right) s^{4/3}-\frac{\tau }{2}\sin \left( \frac{2\pi }{3}\right) s^{2/3}-\frac{\pi }{6}\right) \nonumber \\ \end{aligned}$$
(5.2)

Proof

By definition,

$$\begin{aligned}Q(s)=\dfrac{1}{2i\pi }\displaystyle \int _{i\mathbb {R}}e^{-\frac{\mu ^4}{4}+\tau \frac{\mu ^2}{2}+s\mu }d\mu \end{aligned}$$

The derivative with respect to \(\mu \) of \(\theta _s(\mu )\) is \(\frac{d}{ds}\theta _s(\mu )=\mu ^3-\tau \mu -s\) whose roots are

$$\begin{aligned} \mu _k:=\mu _k(s,\tau )=j^k\root 3 \of {\frac{1}{2}\left( s+\sqrt{s^2-\frac{2^2\tau ^3}{3^3}}\right) }+j^{-k}\root 3 \of {\frac{1}{2}\left( s-\sqrt{s^2-\frac{2^2\tau ^3}{3^3}}\right) } \nonumber \\ \end{aligned}$$
(5.3)

where \(j:=e^{\frac{2\pi }{3}}\) and \(k\in \{0,1,2\}\). We only consider saddle points for \(k=1\) and 2.

Denote by \(\mu _*\) either \(\mu _1\) or \(\mu _2\). One can deform the contour \(i\mathbb {R}\) into a contour \(\mathcal {C}_Q\) so that it passes through \(\mu _1\) and \(\mu _2\) and the following holds \(\Re \left( \theta _s(\mu )-\theta _s(\mu _*)\right) <0\).

See Fig. 3 for the study of the sign of \(\Re \left( \theta _s(\mu )-\theta _s(\mu _*)\right) \) for \(s=100\) and \(\tau =1\). As \(s\rightarrow \infty \) and \(\tau \) is fixed, the algebraic curve \(\Re \left( \theta _s(\mu )-\theta _s(\mu _*)\right) =0\) keeps a similar shape and it is always possible to deform \(i\mathbb {R}\) into \(\mathcal {C}_Q\).

Fig. 3
figure 3

The sign of \(\Re (\theta _s(\mu )-\theta _s(\mu _*))\) for \(\tau =1\) and \(s=100\)

Then

$$\begin{aligned} \begin{array}{rl} Q(s)&{}=\dfrac{e^{-\theta _s(\mu _*)}}{2i\pi }\displaystyle \int _{\mathcal {C}_Q}e^{-\left( \theta _s(\mu )-\theta _s(\mu _*)\right) }d\mu \\ &{}=\dfrac{e^{-\theta _s(\mu _*)}}{2i\pi }\displaystyle \int _{\mathcal {C}_Q}e^{-\left( \frac{3}{2}\mu _*^2(\mu -\mu _*)^2\left( 1-\frac{\tau }{3\mu _*^2}+\frac{2}{3\mu _*}(\mu -\mu _*)+\frac{1}{6\mu _*^2}(\mu -\mu _*)^2\right) \right) }d\mu \end{array} \end{aligned}$$
(5.4)

and one can approximate Q as \(s\rightarrow \infty \) by expanding \(\mu _*\) and by approximating the integral. It follows

$$\begin{aligned} Q(s)\sim \sqrt{\dfrac{2}{3\pi }}s^{-1/3}e^{-\frac{3}{8}s^{4/3}-\frac{\tau }{4}s^{2/3}+\frac{\tau ^2}{6}}\cos \left( \frac{3}{4}\sin \left( \frac{2\pi }{3}\right) s^{4/3}-\frac{\tau }{2}\sin \left( \frac{2\pi }{3}\right) s^{2/3}-\frac{\pi }{6}\right) \nonumber \\ \end{aligned}$$
(5.5)

The same method leads to the asymptotic for P as \(s\rightarrow \infty \). \(\square \)

1.2 Computations for Equation (1.6)

We now prove equation (1.6).

Define \(u(s,\tau ):=\log \left( F(\overrightarrow{\textbf{a}}+s,\tau ,\overrightarrow{\textbf{k}})\right) \) and \(v(s,\tau ):=\dfrac{\partial ^2}{\partial s^2}u(s,\tau )\). According to Proposition 4.2, \(v(s,\tau )=p^T(s)q(s)\). Differentiating v with respect to \(\tau \) and using Eq. (3.11) to express \(\partial _\tau p^T\) and \(\partial _\tau q\), we obtain:

$$\begin{aligned} \partial _\tau v=\dfrac{1}{2}\partial _s\left( p^T\partial _sq-\partial _sp^Tq\right) \end{aligned}$$
(5.6)

Differentiating a second time v with respect to \(\tau \) (again using Eq. (3.11)) yields to

$$\begin{aligned} \partial _{\tau \tau }v=\dfrac{1}{2}\left( 2v\partial _sv+\dfrac{1}{2}\left( p^T\partial _{sss}q+\partial _{sss}p^Tq\right) -\dfrac{1}{2}\partial _s\left( \partial _sp^T\partial _sq\right) \right) \end{aligned}$$
(5.7)

Recall \(v(s,\tau )=p^T(s)q(s)\), then differentiating three times with respect to s the following equation holds:

$$\begin{aligned} \partial _s\left( \partial _sp^T\partial _sq\right) =\dfrac{1}{3}\left( \partial _{sss}v-\left( p^T\partial _{sss}q+\partial _{sss}p^Tq\right) \right) \end{aligned}$$
(5.8)

Replacing \(\partial _s\left( \partial _sp^T\partial _sq\right) \) in Eq. (5.7) and using Eq. (3.10) we obtain:

$$\begin{aligned} \partial _{\tau \tau }v=\partial _s\left( -v\partial _sv-\dfrac{1}{12}\partial _{sss}v+\dfrac{1}{3}\tau \partial _sv\right) . \end{aligned}$$
(5.9)

Replacing v by \(\partial _{ss}u\) and integrating twice with respect to s we prove u satisfies Eq. (1.6).

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Chouteau, T. A Riemann Hilbert Approach to the Study of the Generating Function Associated to the Pearcey Process. Math Phys Anal Geom 26, 10 (2023). https://doi.org/10.1007/s11040-023-09455-8

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