Search arXivSearch

arXiv · 1802.02401

Critical phases in the raise and peel model

Abstract

The raise and peel model (RPM) is a nonlocal stochastic model describing the space and time fluctuations of an evolving one dimensional interface. Its relevant parameter $u$ is the ratio between the rates of local adsorption and nonlocal desorption processes (avalanches) processes. The model at $u=1$ give us the first example of a conformally invariant stochastic model. For small values $u u_0$ it is critical. By calculating the structure function of the height profiles in the reciprocal space we confirm with good precision that indeed $u_0=1$. We establish that at the conformal invariant point $u=1$ the RPM has a roughness transition with dynamical and roughness critical exponents $z=1$ and $\alpha=0$, respectively. For $u>1$ the model is critical with an $u$-dependent dynamical critical exponent $z(u)$ that tends towards zero as $u\to \infty$. However at $1/u=0$ the RPM is exactly mapped into the totally asymmetric exclusion problem (TASEP). This last model is known to be noncritical (critical) for open (periodic) boundary conditions. Our studies indicate that the RPM as $u \to \infty$, due to its nonlocal dynamics processes, has the same large-distance physics no matter what boundary condition we chose. For $u>1$, our analysis show that differently from previous predictions, the region is composed by two distinct critical phases. For $u\leq u < u_c\approx 40$ the height profiles are rough ($\alpha = \alpha(u) >0$), and for $u>u_c$ the height profiles are flat at large distances ($\alpha = \alpha(u) <0$). We also observed that in both critical phases ($u>1$) the RPM at short length scales, has an effective behavior in the Kardar-Parisi-Zhang (KPZ) critical universality class, that is not the true behavior of the system at large length scales.

Explore related subjects

Keep this discovery

BibTeXRIS

D. A. C. Jara, F. C. Alcaraz. 2018-02-07. Critical phases in the raise and peel model. https://doi.org/10.1088/1742-5468/aabc7f

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Universal sampling of spin systems across quenched disorder

Statistical physics extracts macroscopic laws by averaging over the many microscopic degrees of freedom of a system. Disordered systems demand a second and far harder average, one over the quenched randomness itself. The classic analytical routes, the replica and cavity methods, become uncontrolled outside mean-field or tree-like limits, and conventional numerical algorithms like parallel tempering require expensive, independent equilibration for every disorder realization. In this work, we introduce a universal neural variational framework that amortizes inference across the disorder ensemble, eliminating both the need for per-instance Markov chain equilibration and the cost of retraining instance-specific variational ansatzes. Built on an encoder-decoder Transformer architecture, after training once, it produces an explicit approximation to the Boltzmann distribution given previously unseen disorder realizations without further optimization. We validate this framework on 2D Edwards-Anderson models, and apply it to the random-bond Ising model, successfully capturing the Binder cumulant crossings near the Nishimori multicritical point. These results shift the object of variational inference from the single instance to the disorder ensemble, opening a route to frustrated many-body systems where instance-by-instance computation is prohibitive.

cond-mat.stat-mech

Information-Theoretic Characterization of Macroscopic Chaos Emerging from the Chemical Master Equation

Open chemical reaction networks exhibit stochastic concentration dynamics at finite system sizes, whereas their macroscopic limit is governed by deterministic rate equations that can display chaos. In this Letter, we show theoretically that a rate of information loss constructed from two-time mutual information recovers the Kolmogorov-Sinai entropy in the deterministic limit. We verify this result through numerical simulations of a Markov jump process for a three-species system involving seven reactions.

cond-mat.stat-mech

Orientational order on non-orientable domains

We study the statistical properties of passive and active many-body systems with orientational degrees of freedom on non-orientable domains. By rephrasing topological constraints as non-local symmetry relations on an orientable double-cover, we show that non-orientability eliminates global rotational soft modes without acting like an external field. In a passive XY model, this results in topological caging, where orientational fluctuations that exhibit conventional diffusive behavior on a torus saturate on a Klein bottle to a finite value that we compute exactly in the thermodynamic limit. In models of active self-propelled particles with orientational degrees of freedom, topological caging persists despite continuously changing interaction neighborhoods. In an active Ising spin model, non-orientability enforces the coexistence of ordered anti-parallel domains with vanishing global polar order, a state that is absent on orientable domains.

cond-mat.stat-mech