The cutoff profile for exclusion processes in any dimension

Abstract

Consider symmetric simple exclusion processes, with or without Glauber dynamics on the boundary set, on a sequence of connected unweighted graphs $G_N=(V_N,E_N)$ which converge geometrically and spectrally to a compact connected metric measure space. Under minimal assumptions, we prove not only that total variation cutoff occurs at times $t_N=\log |V_N|/(2{\lambda }_1^N)$, where $|V_N|$ is the cardinality of $V_N$, and ${\lambda }_1^N$ is the lowest nonzero eigenvalue of the nonnegative graph Laplacian; but also the limit profile for the total variation distance to stationarity. The assumptions are shown to hold on the $D$-dimensional Euclidean lattices for any $D\ge 1$, as well as on self-similar fractal spaces. Our approach is decidedly analytic and does not use extensive coupling arguments. We identify a new observable in the exclusion process -- the cutoff semimartingales -- obtained by scaling and shifting the density fluctuation fields. Using the entropy method, we prove a functional CLT for the cutoff semimartingales converging to an infinite-dimensional Brownian motion, provided that the process is started from a deterministic configuration or from stationarity. This reduces the original problem to computing the total variation distance between the two versions of Brownian motions, which share the same covariance and whose initial conditions differ only in the coordinates corresponding to the first eigenprojection.

arXiv commentThere is a fatal error in the total variation convergence argument. For exclusion without reservoirs, it is now superseded by the author's submission in September 2026

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