Microcanonical Monte Carlo study of one dimensional self-gravitating lattice gas models

Joao Marcos Maciel, Marco Antônio Amato (), Tarcisio Marciano da Rocha Filho and Annibal D. Figueiredo
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Joao Marcos Maciel: Instituto de Física, Universidade de Brasília
Marco Antônio Amato: Instituto de Física, Universidade de Brasília
Tarcisio Marciano da Rocha Filho: Instituto de Física, Universidade de Brasília
Annibal D. Figueiredo: Instituto de Física, Universidade de Brasília

The European Physical Journal B: Condensed Matter and Complex Systems, 2017, vol. 90, issue 3, 1-8

Abstract: Abstract In this study we present a microcanonical Monte Carlo investigation of one dimensional (1 − d) self-gravitating toy models. We study the effect of hard-core potentials and compare to the results obtained with softening parameters and also the effect of the topology on these systems. In order to study the effect of the topology in the system we introduce a model with the symmetry of motion in a line instead of a circle, which we denominate as 1 /r model. The hard-core particle potential introduces the effect of the size of particles and, consequently, the effect of the density of the system that is redefined in terms of the packing fraction of the system. The latter plays a role similar to the softening parameter ϵ in the softened particles’ case. In the case of low packing fractions both models with hard-core particles show a behavior that keeps the intrinsic properties of the three dimensional gravitational systems such as negative heat capacity. For higher values of the packing fraction the ring model behaves as the potential for the standard cosine Hamiltonian Mean Field model while for the 1 /r model it is similar to the one-dimensional systems. In the present paper we intend to show that a further simplification level is possible by introducing the lattice-gas counterpart of such models, where a drastic simplification of the microscopic state is obtained by considering a local average of the exact N-body dynamics.

Keywords: Statistical; and; Nonlinear; Physics (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1140/epjb/e2017-70550-9

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