Generalizations of Parisi's replica symmetry breaking and overlaps in random energy models

Bernard Derrida, Peter Mottishaw
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Abstract

The random energy model (REM) is the simplest spin glass model which exhibits replica symmetry breaking. It is well known since the 80's that its overlaps are non-selfaveraging and that their statistics satisfy the predictions of the replica theory. All these statistical properties can be understood by considering that the low energy levels are the points generated by a Poisson process with an exponential density. Here we first show how, by replacing the exponential density by a sum of two exponentials, the overlaps statistics are modified. One way to reconcile these results with the replica theory is to allow the blocks in the Parisi matrix to fluctuate. Other examples where the sizes of these blocks should fluctuate include the finite size corrections of the REM, the case of discrete energies and the overlaps between two temperatures. In all these cases, the blocks sizes not only fluctuate but need to take complex values if one wishes to reproduce the results of our replica-free calculations.
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随机能量模型中帕里西复制对称性破缺和重叠的一般化
随机能量模型(REM)是最简单的自旋玻璃模型,表现出复制对称性破缺。自上世纪 80 年代以来,人们就清楚地知道它的重叠是非自平均的,而且其统计特性满足复制理论的预测。考虑到低能级是由具有指数密度的泊松过程产生的点,就可以理解所有这些统计特性。在这里,我们首先展示了用两个指数之和取代指数密度后,重叠统计是如何被修正的。将这些结果与复制理论相协调的一种方法是允许帕里西矩阵中的块发生波动。这些块的大小应该波动的其他例子包括 REM 的有限尺寸修正、离散能量的情况以及两个温度之间的重叠。在所有这些情况下,如果要重现我们的无复制品计算结果,这些块的大小不仅会波动,而且需要取复杂的值。
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