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Analytical Limit Distributions from Random Power-Law Interactions.

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Randomly distributed power-law interactions in nature, like gravity, do not follow expected Gauss or Lévy distributions. New research reveals intermediate distributions influenced by source concentration and probe size, explained through theoretical articulation and simulations.

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Area of Science:

  • Physics
  • Statistical Mechanics
  • Complex Systems

Background:

  • Natural phenomena often exhibit power-law interactions (e.g., gravity, electrostatics).
  • Randomly distributed sources of these fields are typically expected to result in superposed interactions following Gauss or Lévy distributions.
  • However, observed distributions can deviate from these theoretical expectations.

Purpose of the Study:

  • To derive an analytic expression for the actual distributions of superposed power-law interactions.
  • To identify novel limit distributions that lie between Gauss and Lévy distributions.
  • To theoretically articulate the origins of these non-Gauss and non-Lévy distributions.

Main Methods:

  • Development of an analytic expression for interaction distributions.
  • Comparison with results from numerical simulations.
  • Theoretical articulation of distribution origins.

Main Results:

  • Identified novel limit distributions intermediate to Gauss and Lévy distributions.
  • Demonstrated dependence of these distributions on physical parameters like source concentration and probe size.
  • Theoretically explained the emergence of non-Gauss and non-Lévy distributions.

Conclusions:

  • The superposed interactions of randomly distributed power-law sources do not always conform to Gauss or Lévy distributions.
  • New intermediate distributions are predicted, governed by system parameters.
  • This work provides a theoretical framework for understanding complex field interactions in nature.