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Large deviation function of the partially asymmetric exclusion process.

D S Lee1, D Kim

  • 1Department of Physics, Seoul National University, Seoul 151-742, Korea.

Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
|April 24, 2002
PubMed
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The study generalizes the large deviation function for the totally asymmetric exclusion process to the partially asymmetric case. This work provides insights into the scaling behavior and finite-size corrections in these complex systems.

Area of Science:

  • Statistical Mechanics
  • Non-equilibrium Physics
  • Stochastic Processes

Background:

  • The totally asymmetric exclusion process (TASEP) is a fundamental model in statistical mechanics.
  • Derrida and Lebowitz recently derived a large deviation function for the TASEP.
  • Understanding the behavior of exclusion processes under varying asymmetry is crucial.

Purpose of the Study:

  • To generalize the large deviation function for the totally asymmetric exclusion process to the partially asymmetric exclusion process.
  • To investigate the impact of the asymmetry parameter on the scaling limit.
  • To determine finite-size corrections to universal quantities.

Main Methods:

  • Mathematical derivation and generalization of the large deviation function.

Related Experiment Videos

  • Analysis in the scaling limit.
  • Calculation of leading-order finite-size corrections.
  • Main Results:

    • The asymmetry parameter rescales the scaling variable in a straightforward manner.
    • The study provides the generalized large deviation function for the partially asymmetric exclusion process.
    • Leading-order finite-size corrections to the universal scaling function and cumulant ratio were obtained.

    Conclusions:

    • The generalization successfully extends the understanding of large deviation properties in exclusion processes.
    • The findings offer a more comprehensive picture of TASEP behavior under varying degrees of asymmetry.
    • This work contributes to the theoretical framework for analyzing complex stochastic systems.