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Irreversibility in linear systems with colored noise.

Grzegorz Gradziuk1, Gabriel Torregrosa1, Chase P Broedersz2

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This study generalizes measures of time irreversibility for stochastic systems. It shows how to analyze systems with colored noise, not just white noise, using a unified theoretical framework.

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Area of Science:

  • Statistical Mechanics
  • Non-equilibrium Dynamics
  • Stochastic Processes

Background:

  • Time irreversibility quantifies deviation from thermal equilibrium in stochastic systems.
  • Real-world systems often exhibit long-lived temporal correlations in noise (colored noise), unlike idealized white noise.
  • Existing measures of irreversibility are primarily developed for white noise approximations.

Purpose of the Study:

  • To analyze the impact of temporal noise correlations on established irreversibility measures.
  • To generalize the theoretical framework for white-noise-driven systems to accommodate colored noise.
  • To provide a unified approach for quantifying irreversibility in systems with correlated noise.

Main Methods:

  • Generalization of theoretical framework for white-noise-driven systems to colored noise.
  • Expression of irreversibility measures (autocorrelation function, area enclosing rates, mean phase space velocity) using Lyapunov equations.
  • Comparison of colored noise results with their white-noise limit values.

Main Results:

  • Demonstrated a natural generalization of white-noise irreversibility measures to colored noise.
  • Expressed key irreversibility indicators in terms of solutions to a Lyapunov equation.
  • Quantified the deviation of colored noise effects from the white-noise approximation.

Conclusions:

  • The theoretical framework for analyzing time irreversibility can be effectively extended to systems with colored noise.
  • Lyapunov equations provide a powerful tool for calculating irreversibility measures in the presence of temporal correlations.
  • This work offers a more realistic approach to quantifying nonequilibrium dynamics in systems with realistic noise characteristics.