## Infinite rate symbiotic branching on the real line: the tired frogs model.(English. French summary)Zbl 1465.60085

This paper treats a special case of the infinite rate symbiotic branching process on the real line with different motion speeds for the two types, i.e., the tired frogs model as the authors call it. The modeling interpretation behind the setting is quite stimulating and interesting. Initially the frogs on the positive half-line are dormant while those on the negative half-line are awake and move according to the heat flow. At the interface, the incoming wake frogs try to wake up the dormant frogs and succeed with a probability proportional to their amount among the total amount of involved frogs at the specific site. Otherwise, the incoming frogs also fall asleep. By regarding this frog model as the limit of approximating processes and computing the structure of jumps, the authors show that their frog model can be described by a stochastic partial differential equation on the real line with a jump-type noise.
More precisely, let $$M_F$$ be the space of finite measures on $${\mathbb R}$$ equipped with the weak topology, and $$D_U$$ is the space of cadlag functions $$[0, \infty) \to U$$ equipped with the Skorokhod topology for any metric space $$U$$. $$C_b^2({\mathbb R})$$ denotes the space of twice continuously differentiable functions with bounded first and second derivative. Let $$X_t^1$$ be a process satisfying $\partial_t X_t^1 = \frac{1}{2} \partial_x^2 X_t^1 \qquad \text{on} \quad (- \infty, z_n) \quad \text{for} \quad \tau^n < t <\tau^{n+1}$ with $$X_t^1(x) =0$$ for all $$x \geq \bar{\ell}_t$$, where $$0 < z_1 < z_2 < \cdots$$. $$( X_t^1, X_t^2)$$, $$t \geq 0$$, are $$M_F$$-valued processes, and initially $$X_0^2 = \sum_{i \geq 1} x_i \delta_{ z_i}$$ with $\sum_i x_i < \infty, \qquad \text{and} \qquad \bar{\ell}_t := \sup \{ x : \, X_t^2 ( ( - \infty, x ]) = 0 \}.$ Define $$\tau^i := \inf \{ t \geq 0: \, \bar{\ell}_t = z_i \}$$. $$X_t^2$$ satisfies $X_t^2( \{ z_n \} ) = X_0^2 ( \{ z_n \} ) + \int_{\tau^n}^t \frac{1}{2} \partial_x^2 X_s^1 ( \{ z_n \}) ds \qquad \text{for} \quad \tau^n < t < \tau^{n+1}.$ Assume that (i) $$X_t^1$$ is an absolutely continuous finite measure with compact support in $$(- \infty, 0]$$ and with bounded density $$X_0^1(x)$$; (ii) $$X_0^2$$ is an absolutely continuous finite measure with support in $$[0,1]$$ and with density $$X_0^2(x)$$; (iii) $$X_0^2(x) > 0$$ for all $$x \in (0,1)$$ and $$X_0^2(x)$$ is continuous in $$x \in (0,1)$$. The family of approximating processes $$( X^{1, \eta}, X^{2, \eta})$$ are defined as follows. For any $$\eta > 0$$, define $X_0^{1, \eta} = X_0^1, \qquad x_i^{2,\eta} = X_0^2 ( ( (i-1)\eta, i \eta] ) \quad \text{for} \quad i \geq 1, \qquad X_0^{2, \eta} = \sum_{i \geq 1} x_i^{2, \eta} \delta_{i \eta}.$ As a matter of fact, they show that $$( X^{1, \eta}, X^{2, \eta})$$, $$\eta > 0$$, is tight in $$D_{M_F \times M_F}$$ and prove that any limit point for $$\eta \searrow 0+$$ is a weak solution to the following system of stochastic partial differential equations: for any $$\phi_1, \phi_2 \in C_b^2({\mathbb R})$$, \begin{align*} &X_t^1(\phi_1) = X_0^1(\phi_1) + \int_0^t X_s^1 ( \frac{1}{2} \phi_1'') ds + M_t(\phi_1), \\ &X_t^2(\phi_2) = X_0^2(\phi_2) - M_t(\phi_2), \end{align*} where $$M_t(\phi_i)$$, $$i=1,2$$, are martingales derived from the orthogonal martingale measure $${\mathcal M}$$ by $M_t(\phi) := \int_-^t \int_0^{\infty} Y_{s-} \phi( \ell_{s-} ) I_{ [0, i(X_{s-})]}(a) {\mathcal M}(ds, da),$ $$\ell_s := \inf \{ x : \, X_s^2( ( - \infty, x]) > 0 \} \wedge 1$$ anf $$Y_s = X_s^2( \{ \ell_s \} )$$, where $${\mathcal N}(dt, dr)$$ is a Poisson point process on $${\mathbb R}_+^2$$ with intensity measure $${\mathcal N}'(dt, dr)$$ $$=$$ $$dt dr$$, and $${\mathcal M}$$ $$:=$$ $${\mathcal N} - {\mathcal N}'$$.
For other related works, see, e.g., [J. Blath et al., Ann. Probab. 44, No. 2, 807–866 (2016; Zbl 1347.60119)] for a symbiotic branching model; [A. Klenke and L. Mytnik, Probab. Theory Relat. Fields 154, No. 3–4, 533–584 (2012; Zbl 1266.60160)] for infinite rate mutually catalytic branching in infinitely many colonies; see also [the authors, Ann. Probab. 40, No. 1, 103–129 (2012; Zbl 1244.60088)] for the longtime behaviors of the same model as the above.

### MSC:

 60K35 Interacting random processes; statistical mechanics type models; percolation theory 60J80 Branching processes (Galton-Watson, birth-and-death, etc.) 60J68 Superprocesses 60H15 Stochastic partial differential equations (aspects of stochastic analysis)

### Citations:

Zbl 1347.60119; Zbl 1266.60160; Zbl 1244.60088
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### References:

 [1] J. Blath, M. Hammer and M. Ortgiese. The scaling limit of the interface of the continuous-space symbiotic branching model. Ann. Probab. 44 (2) (2016) 807-866. · Zbl 1347.60119 [2] D. A. Dawson. Measure-valued Markov processes. In École d’Été de Probabilités de Saint-Flour XXI - 1991 1-260 P. L. Hennequin (Ed.). Lecture Notes in Mathematics 1541. Springer, Berlin, 1993. · Zbl 0799.60080 [3] D. A. Dawson and E. A. Perkins. Long-time behavior and coexistence in a mutually catalytic branching model. Ann. Probab. 26 (3) (1998) 1088-1138. · Zbl 0938.60042 [4] L. Döring and L. Mytnik. Mutually catalytic branching processes and voter processes with strength of opinion. ALEA Lat. Am. J. Probab. Math. Stat. 9 (2012) 1-51. · Zbl 1277.60138 [5] A. M. Etheridge and K. Fleischmann. Compact interface property for symbiotic branching. Stochastic Process. Appl. 114 (1) (2004) 127-160. · Zbl 1072.60086 [6] S. N. Ethier and T. G. Kurtz. Markov Processes: Characterization and Convergence. John Wiley & Sons Inc., New York, 1986. · Zbl 0592.60049 [7] L. R. Fontes, F. P. Machado and A. Sarkar. The critical probability for the frog model is not a monotonic function of the graph. J. Appl. Probab. 41 (1) (2004) 292-298. · Zbl 1051.60095 [8] N. Gantert and P. Schmidt. Recurrence for the frog model with drift on $$\mathbb{Z}$$. Markov Process. Related Fields 15 (1) (2009) 51-58. · Zbl 1172.60030 [9] N. Ikeda and S. Watanabe. Stochastic Differential Equations and Diffusion Processes, 2nd edition. North-Holland Mathematical Library 24. North-Holland Publishing Co., Amsterdam, 1989. · Zbl 0684.60040 [10] C. G. J. Jacobi. Suite des notices sur les fonctions elliptiques. (V. p. 192). J. Reine Angew. Math. 3 (1828) 303-310. [11] J. Jacod and A. N. Shiryaev. Limit Theorems for Stochastic Processes, 2nd edition. Grundlehren der Mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences] 288. Springer-Verlag, Berlin, 2003. · Zbl 1018.60002 [12] A. Jakubowski. On the Skorokhod topology. Ann. Inst. Henri Poincaré Probab. Stat. 22 (3) (1986) 263-285. · Zbl 0609.60005 [13] A. Klenke and L. Mytnik. Infinite rate mutually catalytic branching. Ann. Probab. 38 (4) (2010) 1690-1716. · Zbl 1204.60081 [14] A. Klenke and L. Mytnik. Infinite rate mutually catalytic branching in infinitely many colonies: Construction, characterization and convergence. Probab. Theory Related Fields 154 (3-4) (2012) 533-584. · Zbl 1266.60160 [15] A. Klenke and L. Mytnik. Infinite rate mutually catalytic branching in infinitely many colonies: The longtime behavior. Ann. Probab. 40 (1) (2012) 103-129. · Zbl 1244.60088 [16] A. Klenke and M. Oeler. A Trotter type approach to infinite rate mutually catalytic branching. Ann. Probab. 38 (2) (2010) 479-497. · Zbl 1191.60112 [17] E. Kosygina and M. P. W. Zerner. A zero-one law for recurrence and transience of frog processes. Probab. Theory Related Fields 168 (1-2) (2017) 317-346. · Zbl 1372.60047 [18] L. Mytnik. Uniqueness for a mutually catalytic branching model. Probab. Theory Related Fields 112 (2) (1998) 245-253. · Zbl 0912.60076 [19] S. Yu. Popov. Frogs and some other interacting random walks models. In Discrete Random Walks (Paris, 2003) 277-288. Discrete Math. Theor. Comput. Sci. Proc., AC. Assoc. Discrete Math. Theor. Comput. Sci., Nancy, 2003. · Zbl 1034.60089 [20] J. Walsh. An introduction to stochastic partial differential equations. Lecture Notes in Math. 1180 (1986) 265-439. [21] E. T. Whittaker and G. N. Watson. A Course of Modern Analysis, 4th edition. Cambridge University Press, Cambridge, 1966. · JFM 45.0433.02
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