Convergence of the environment seen from geodesics in exponential last-passage percolation

A well-known question in planar first-passage percolation concerns the convergence of the empirical distribution of weights as seen along geodesics. We demonstrate this convergence for an explicit model, directed last-passage percolation on \mathbb{Z}^{2} with i.i.d. exponential weights, and provide explicit formulae for the limiting distributions, which depend on the asymptotic direction. For example, for geodesics in the direction of the diagonal, the limiting weight distribution has density (1/4+x/2+x^{2}/8)e^{-x} , and so is a mixture of Gamma( 1,1 ), Gamma( 2,1 ), and Gamma( 3,1 ) distributions with weights 1/4 , 1/2 , and 1/4 respectively. More generally, we study the local environment as seen from vertices along geodesics (including information about the shape of the path and about the weights on and off the path in a local neighborhood). We consider finite geodesics from (0,0) to n\boldsymbol{\rho} for some vector \boldsymbol{\rho} in the first quadrant, in the limit as n\to\infty , as well as semi-infinite geodesics in direction \boldsymbol{\rho} . We show almost sure convergence of the empirical distributions of the environments along these geodesics, as well as convergence of the distributions of the environment around a typical point in these geodesics, to the same limiting distribution, for which we give an explicit description.We make extensive use of a correspondence with TASEP as seen from an isolated second-class particle for which we prove new results concerning ergodicity and convergence to equilibrium. Our analysis relies on geometric arguments involving estimates for last-passage times, available from the integrable probability literature.

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Publication Details

Journal
Journal of the European Mathematical Society
Published
2025-01-27
DOI
https://doi.org/10.4171/jems/1594
Primary Topic
Random Matrices and Applications
Type
article
Field-Weighted Citation Impact
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article

Convergence of the environment seen from geodesics in exponential last-passage percolation

Allan Sly, Lingfu Zhang, Jean Martin
Journal of the European Mathematical Society
Random Matrices and Applications
article

Convergence of the environment seen from geodesics in exponential last-passage percolation

Allan Sly, Lingfu Zhang, Jean Martin
article en

Abstract

A well-known question in planar first-passage percolation concerns the convergence of the empirical distribution of weights as seen along geodesics. We demonstrate this convergence for an explicit model, directed last-passage percolation on \mathbb{Z}^{2} with i.i.d. exponential weights, and provide explicit formulae for the limiting distributions, which depend on the asymptotic direction. For example, for geodesics in the direction of the diagonal, the limiting weight distribution has density (1/4+x/2+x^{2}/8)e^{-x} , and so is a mixture of Gamma( 1,1 ), Gamma( 2,1 ), and Gamma( 3,1 ) distributions with weights 1/4 , 1/2 , and 1/4 respectively. More generally, we study the local environment as seen from vertices along geodesics (including information about the shape of the path and about the weights on and off the path in a local neighborhood). We consider finite geodesics from (0,0) to n\boldsymbol{\rho} for some vector \boldsymbol{\rho} in the first quadrant, in the limit as n\to\infty , as well as semi-infinite geodesics in direction \boldsymbol{\rho} . We show almost sure convergence of the empirical distributions of the environments along these geodesics, as well as convergence of the distributions of the environment around a typical point in these geodesics, to the same limiting distribution, for which we give an explicit description.We make extensive use of a correspondence with TASEP as seen from an isolated second-class particle for which we prove new results concerning ergodicity and convergence to equilibrium. Our analysis relies on geometric arguments involving estimates for last-passage times, available from the integrable probability literature.

Journal of the European Mathematical SocietyVol. 27(3)
California Institute of Technology (US), Princeton University (US), University of Oxford (GB)
National Science Foundation
Openalex Percentile: Top 98%
Random Matrices and Applications
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