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arXiv 1303.0677cond-mat.stat-mech
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薄图上具有(17)个不可见态的Potts模型

Potts Models with (17) Invisible States on Thin Graphs

D. A. Johnston, R. P. K. C. M. Ranasinghe

摘要:

相变的阶数通常由在相变点的对称性破缺的性质和所考虑的模型的维数决定。例如,二维q态Potts模型在q = 2,3,4时表现出二阶连续跃迁,而在较高q时表现出一阶跃迁。Tamura等人最近介绍了具有“不可见”状态的Potts模型,这些状态对熵有贡献,但对内能没有贡献,并指出添加这种不可见状态可以将连续跃迁转变为一阶跃迁。这在布拉格-威廉姆斯型平均场计算和2D蒙特卡罗模拟中都观察到。有人认为,在观察到与通常方案不一致的跃迁顺序的情况下,改变跃迁顺序的隐形状态机制可能会发挥作用。在本文中,我们注意到,一个替代的平均场的方法,采用3-规则的随机(“薄”)的图形也显示这种变化的顺序的过渡作为不可见的状态的数量是不同的,虽然所需的状态的数量进行嬗变,17个不可见的状态时,有2个可见的状态,是远远高于在布拉格-威廉姆斯的情况。通过使用具有2个可见态和r个不可见态的Potts模型与Blume-Emery-Griffiths(BEG)模型的等价性来进行计算,因此副产品是BEG模型在薄随机图上的解。

英文摘要:

The order of a phase transition is usually determined by the nature of the symmetry breaking at the phase transition point and the dimension of the model under consideration. For instance, q-state Potts models in two dimensions display a second order, continuous transition for q = 2,3,4 and first order for higher q. Tamura et al recently introduced Potts models with "invisible" states which contribute to the entropy but not the internal energy and noted that adding such invisible states could transmute continuous transitions into first order transitions. This was observed both in a Bragg-Williams type mean-field calculation and 2D Monte-Carlo simulations. It was suggested that the invisible state mechanism for transmuting the order of a transition might play a role where transition orders inconsistent with the usual scheme had been observed. In this paper we note that an alternative mean-field approach employing 3-regular random ("thin") graphs also displays this change in the order of the transition as the number of invisible states is varied, although the number of states required to effect the transmutation, 17 invisible states when there are 2 visible states, is much higher than in the Bragg-Williams case. The calculation proceeds by using the equivalence of the Potts model with 2 visible and r invisible states to the Blume-Emery-Griffiths (BEG) model, so a by-product is the solution of the BEG model on thin random graphs.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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