Quasi-Geodesics in Integrable and Non-Integrable Exclusion Processes

Abstract Backwards geodesics for TASEP were introduced in [30]. We consider flat initial conditions and show that under proper scaling the end-point of the geodesic converges to maximizer argument of the $$\hbox {Airy}_2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mtext>Airy</mml:mtext> <mml:mn>2</mml:mn> </mml:msub> </mml:math> process minus a parabola. We generalize its definition to generic non-integrable models including ASEP and speed changed ASEP (call it quasi-geodesics). We numerically verify that its end-point is universal, where the scaling coefficients are analytically computed through the KPZ scaling theory.

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Publication Details

Journal
Journal of Statistical Physics
Published
2025-08-11
DOI
https://doi.org/10.1007/s10955-025-03488-9
Primary Topic
Random Matrices and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Quasi-Geodesics in Integrable and Non-Integrable Exclusion Processes

Min Liu, Patrik L. Ferrari
Journal of Statistical Physics
Random Matrices and Applications
article

Quasi-Geodesics in Integrable and Non-Integrable Exclusion Processes

Min Liu, Patrik L. Ferrari
article en

Abstract

Abstract Backwards geodesics for TASEP were introduced in [30]. We consider flat initial conditions and show that under proper scaling the end-point of the geodesic converges to maximizer argument of the $$\hbox {Airy}_2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mtext>Airy</mml:mtext> <mml:mn>2</mml:mn> </mml:msub> </mml:math> process minus a parabola. We generalize its definition to generic non-integrable models including ASEP and speed changed ASEP (call it quasi-geodesics). We numerically verify that its end-point is universal, where the scaling coefficients are analytically computed through the KPZ scaling theory.

Journal of Statistical PhysicsVol. 192(8)
University of Bonn (DE)
Deutsche Forschungsgemeinschaft
Reduced inequalities
Openalex Percentile: Top 80%
Random Matrices and Applications
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