Noisy multistate voter model for flocking in finite dimensions.

Area of Science:

• Statistical physics
• Complex systems
• Collective behavior

Background:

• Self-propelled particles exhibit complex collective behaviors.
• Voter models describe opinion dynamics and alignment.
• Noise and particle motion significantly influence system order.

Purpose of the Study:

• To investigate the impact of particle motion and noise on collective alignment.
• To analyze the order-disorder phase transition in a model of self-propelled particles.
• To determine how particle speed and noise amplitude affect global order.

Main Methods:

• Simulating a model of self-propelled particles with pairwise copying interactions and noise.
• Analyzing the system's steady states in static (v=0) and dynamic (v>0) scenarios.
• Investigating the finite-size scaling of the transition noise in 1D and 2D lattices.

Main Results:

• Static particles (v=0) exhibit complete disorder for any positive noise (η>0) in the thermodynamic limit.
• Full order is achieved for static particles only in the absence of noise (η=0).
• Moving particles (v>0) enable an ordered phase, with an order-disorder phase transition at a critical noise amplitude (η_c > 0) proportional to particle speed (v).

Conclusions:

• Particle motion is crucial for sustaining global order in systems with voter-like interactions.
• The emergence of an ordered phase is a direct consequence of particle dynamics.
• The study provides insights into phase transitions driven by motion in interacting particle systems.