## Abstract

In the single-source sandpile model, a number N grains of sand are positioned at a central vertex on the 2-dimensional grid Z^2. We study the stabilisation of this configuration for a stochastic sandpile model based on a parameter M∈N. In this model, if a vertex has at least 4M grains of sand, it topples, sending k grains of sand to each of its four neighbours, where k is drawn according to some random distribution γ with support {1,⋯,M}. Topplings continue, a new random number k being drawn each time, until we reach a stable configuration where all the vertices have less than 4M grains. This model is a slight variant on the one introduced by Kim and Wang.

We analyse the stabilisation process described above as N tends to infinity (for fixed M), for various probability distributions γ. We focus on two global parameters of the system, referred to as radius and avalanche numbers. The radius number is the greatest distance from the origin to which grains are sent during the stabilisation, while the avalanche number is the total number of topplings made. Our simulations suggest that both of these numbers have fairly simple asymptotic behaviours as functions of γ, N and M as N tends to infinity. We also provide a more detailed analysis in the case where γ is the binomial distribution with parameter p, in particular when p tends to 1. We exhibit a phase transition in that regime at the scale p∼1/N.

We analyse the stabilisation process described above as N tends to infinity (for fixed M), for various probability distributions γ. We focus on two global parameters of the system, referred to as radius and avalanche numbers. The radius number is the greatest distance from the origin to which grains are sent during the stabilisation, while the avalanche number is the total number of topplings made. Our simulations suggest that both of these numbers have fairly simple asymptotic behaviours as functions of γ, N and M as N tends to infinity. We also provide a more detailed analysis in the case where γ is the binomial distribution with parameter p, in particular when p tends to 1. We exhibit a phase transition in that regime at the scale p∼1/N.

Original language | English |
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Type | ArXiv preprint |

Number of pages | 25 |

Publication status | Published - 22 Aug 2022 |