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Multi-point correlations for two-dimensional coalescing or annihilating random walks

Published online by Cambridge University Press:  16 January 2019

James Lukins*
Affiliation:
University of Warwick
Roger Tribe*
Affiliation:
University of Warwick
Oleg Zaboronski*
Affiliation:
University of Warwick
*
* Postal address: Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK.
* Postal address: Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK.
* Postal address: Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK.

Abstract

In this paper we consider an infinite system of instantaneously coalescing rate 1 simple symmetric random walks on ℤ2, started from the initial condition with all sites in ℤ2 occupied. Two-dimensional coalescing random walks are a `critical' model of interacting particle systems: unlike coalescence models in dimension three or higher, the fluctuation effects are important for the description of large-time statistics in two dimensions, manifesting themselves through the logarithmic corrections to the `mean field' answers. Yet the fluctuation effects are not as strong as for the one-dimensional coalescence, in which case the fluctuation effects modify the large time statistics at the leading order. Unfortunately, unlike its one-dimensional counterpart, the two-dimensional model is not exactly solvable, which explains a relative scarcity of rigorous analytic answers for the statistics of fluctuations at large times. Our contribution is to find, for any N≥2, the leading asymptotics for the correlation functions ρN(x1,…,xN) as t→∞. This generalises the results for N=1 due to Bramson and Griffeath (1980) and confirms a prediction in the physics literature for N>1. An analogous statement holds for instantaneously annihilating random walks. The key tools are the known asymptotic ρ1(t)∼logt∕πt due to Bramson and Griffeath (1980), and the noncollision probability 𝒑NC(t), that no pair of a finite collection of N two-dimensional simple random walks meets by time t, whose asymptotic 𝒑NC(t)∼c0(logt)-(N2) was found by Cox et al. (2010). We re-derive the asymptotics, and establish new error bounds, both for ρ1(t) and 𝒑NC(t) by proving that these quantities satisfy effective rate equations; that is, approximate differential equations at large times. This approach can be regarded as a generalisation of the Smoluchowski theory of renormalised rate equations to multi-point statistics.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 2018 

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References

[1]Bramson, M. and Griffeath, D. (1980). Asymptotics for interacting particle systems on ℤd. Z. Wahrscheinlichkeitsth. 53, 183196.Google Scholar
[2]Cox, J. T. and Griffeath, D. (1986). Diffusive clustering in the two-dimensional voter model. Ann. Prob. 14, 347370.Google Scholar
[3]Cox, J. T. and Perkins, E. A. (2004). An application of the voter model—super-Brownian motion invariance principle. Ann. Inst. H. Poincaré Prob. Statist. 40, 2532.Google Scholar
[4]Cox, J. T., Merle, M. and Perkins, E. (2010). Coexistence in a two-dimensional Lotka-Volterra model. Electron. J. Prob. 15, 11901266.Google Scholar
[5]De Bruijn, N. G. (1955). On some multiple integrals involving determinants. J. Indian Math. Soc. 19, 133151.Google Scholar
[6]Durrett, R. (1989). Lecture Notes on Particle Systems and Percolation. Wadsworth and Brooks, Pacific Grove, CA.Google Scholar
[7]Gaudillière, A. (2009). Collision probability for random trajectories in two dimensions. Stoch. Process. Appl. 119, 775810.Google Scholar
[8]Grabiner, D. J. (1999). Brownian motion in a Weyl chamber, non-colliding particles, and random matrices. Ann. Inst. H. Poincaré Statist. 35, 177204.Google Scholar
[9]Krapivsky, P. L., Ben-Naim, E. and Redner, S. (1994). Kinetics of heterogeneous single-species annihilation. Phys. Rev. E 50, 24742481.Google Scholar
[10]Lukins, J. (2017). Coalescing Particle Systems. Doctoral Thesis, University of Warwick. In preparation.Google Scholar
[11]Munasinghe, R., Rajesh, R., Tribe, R. and Zaboronski, O. (2006). Multi-scaling of the n-point density function for coalescing Brownian motions. Commun. Math. Phys. 268, 717725.Google Scholar
[12]Munasinghe, R. M., Rajesh, R. and Zaboronski, O. V. (2006). Multiscaling of correlation functions in single species reaction-diffusion systems. Phys. Rev. E 73, 051103.Google Scholar
[13]Révész, P. (2013). Random Walk in Random and Non-Random Environments, 3rd edn. World Scientific, Hackensack, NJ.Google Scholar
[14]Sawyer, S. (1979). A limit theorem for patch sizes in a selectively-neutral migration model. J. Appl. Prob. 16, 482495.Google Scholar
[15]Steinfeld, J. I., Francisco, J. S. and Hase, W. L. (1989). Chemical Kinetics and Dynamics. Prentice Hall, Englewood Cliffs, NJ.Google Scholar
[16]Täuber, U. C., Howard, M. and Vollmayr-Lee, B. P. (2005). Applications of field-theoretic renormalization group methods to reaction–diffusion problems. J. Phys. A 38, R79R131.Google Scholar
[17]Tribe, R. and Zaboronski, O. (2011). Pfaffian formulae for one dimensional coalescing and annihilating systems. Electron. J. Prob. 16, 20812103.Google Scholar
[18]Van den Berg, J. and Kesten, H. (2000). Asymptotic density in a coalescing random walk model. Ann. Prob. 28, 303352.Google Scholar
[19]Van den Berg, J. and Kesten, H. (2002). Randomly coalescing random walk in dimension d≥3. In In and out of equilibrium, (Progr. Prob. 51). Birkhäuser, Boston, MA, pp. 145.Google Scholar