2014
DOI: 10.1007/s00220-014-1985-1
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Time Scale Separation and Dynamic Heterogeneity in the Low Temperature East Model

Abstract: We consider the non-equilibrium dynamics of the East model, a linear chain of 0-1 spins evolving under a simple Glauber dynamics in the presence of a kinetic constraint which forbids flips of those spins whose left neighbor is 1. We focus on the glassy effects caused by the kinetic constraint as q ↓ 0, where q is the equilibrium density of the 0's. In the physical literature this limit is equivalent to the zero temperature limit. We first prove that, for any given L = O(1/q), the divergence as q ↓ 0 of three b… Show more

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Cited by 25 publications
(67 citation statements)
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“…The proof of this proposition, which is similar to that of [14,Proposition 3.4], is deferred to the Appendix.…”
Section: )mentioning
confidence: 89%
See 1 more Smart Citation
“…The proof of this proposition, which is similar to that of [14,Proposition 3.4], is deferred to the Appendix.…”
Section: )mentioning
confidence: 89%
“…It is not difficult to see that in any dimension q c (Z d , U ) = 0. For d = 1, it was first proved in [1] that the relaxation time T rel (q) is finite for any q ∈ (0, 1], and it was later shown (see [1,10,14]) that it diverges as exp 1 + o (1) log(1/q) 2 2 log 2 as q ↓ 0. A similar scaling was later proved in any dimension d 1, see [15].…”
Section: Introductionmentioning
confidence: 99%
“…Consider for example the one-dimensional East model [15] (and [13] for a review) for which a site can be updated iff its left neighbour is empty, namely U = {{− e 1 }}. As q ↓ 0, it holds E East µ (τ 0 ) = e (Θ(log q) 2 ) (1.2) and the scaling holds for T rel , see [1,8,9] where the sharp value of the constant has been determined. This divergence is much faster than for the corresponding Ubootstrap model, for which it holds T = Θ(1/q).…”
Section: Introductionmentioning
confidence: 99%
“…Instead, there is not a direct connection with the cellular automata which allows to compute an upper bound on the time scales, and the best general upper bound is E µ (τ 0 ) ≤ exp(cL d c ) ( [9]). Though this bound has been refined for certain models leading in some cases the sharp behavior [9][10][11], the techniques are always ad hoc and valid only for very special choices of the constraints.…”
Section: Resultsmentioning
confidence: 99%