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Directional synchrony among self-propelled particles under spatial influence.

Suvam Pal1, Gourab Kumar Sar1, Dibakar Ghosh1

  • 1Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India.

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Summary
This summary is machine-generated.

This study explores directional synchrony in self-propelled particles, revealing how spatial distance influences collective behavior and enabling analytical prediction of phase transitions in active systems.

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Area of Science:

  • Physics
  • Complex Systems
  • Statistical Mechanics

Background:

  • Collective phenomena like synchronization are widespread in nature and technology.
  • Interacting active particles exhibit exotic phase transitions, a key research area.
  • Understanding directional synchrony is crucial for modeling emergent behaviors.

Purpose of the Study:

  • To investigate directional synchrony in self-propelled particles with direction coupled to spatial degrees of freedom.
  • To analyze the impact of short- and long-ranged spatial influences on directional coupling.
  • To develop an analytical approximation for predicting critical transition points.

Main Methods:

  • Modeling self-propelled particles with directionally coupled movement within a bounded region.
  • Investigating phase transitions under varying spatial influence ranges (short and long).
  • Developing and applying an approximation technique for analytical solutions, validated by numerical simulations.

Main Results:

  • Characterization of phase transitions in directional synchrony for self-propelled particles.
  • Successful development of an approximation technique to analytically determine critical transition points.
  • Numerical simulations confirm the analytical findings regarding spatial influence on synchrony.

Conclusions:

  • The study provides insights into directional synchrony in active matter systems.
  • The developed approximation technique offers a valuable tool for analyzing critical phenomena.
  • Findings have implications for understanding and modeling collective behaviors in biological and robotic systems.