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Journal Articles

Stochastic dynamics toward the steady state of self-gravitating systems

Tashiro, Toru*; Tatekawa, Takayuki

Numerical Simulations of Physical and Engineering Processes, p.301 - 318, 2011/09

The behavior of a self-gravitating system (SGS) is described using the equilibrium statistical mechanics. Although the behavior of the system is analytically solved for the spherically symmetric system, it is known that mass density in the central region and/or total mass of the system sometimes diverges. King model which is derived by modification of the statistical mechanics can explain the distribution without these difficulties. We construct a theory which can explain the dynamics toward the special steady state described by the King model. SGSs require quite long time for relaxation. Furthermore, we must compute interaction of all particle pairs. By these reasons, we require huge computation power for numerical simulation of the evolution of SGS. So we have applied special-purpose processor for computation of the interaction. From the numerical simulations of SGS, we have confirmed that our theory is appropriate for description of realistic density distribution.

Journal Articles

Brownian dynamics around the core of self-gravitating systems

Tashiro, Toru*; Tatekawa, Takayuki

Journal of the Physical Society of Japan, 79(6), p.063001_1 - 063001_4, 2010/06

 Times Cited Count:7 Percentile:46.56(Physics, Multidisciplinary)

We derive the non-Maxwellian distribution of self-gravitating $$N$$-body systems around the core by a model based on the random process with the additive and the multiplicative noise. The number density can be obtained through the steady state solution of the Fokker-Planck equation corresponding to the random process. We exhibit that the number density becomes equal to that of the King model around the core by adjusting the friction coefficient and the intensity of the multiplicative noise. We also show that our model can be applied in the system which has a heavier particle. Moreover, we confirm the validity of our model by comparing with our numerical simulation.

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