{"title":"随机能量模型中帕里西复制对称性破缺和重叠的一般化","authors":"Bernard Derrida, Peter Mottishaw","doi":"arxiv-2408.15125","DOIUrl":null,"url":null,"abstract":"The random energy model (REM) is the simplest spin glass model which exhibits\nreplica symmetry breaking. It is well known since the 80's that its overlaps\nare non-selfaveraging and that their statistics satisfy the predictions of the\nreplica theory. All these statistical properties can be understood by\nconsidering that the low energy levels are the points generated by a Poisson\nprocess with an exponential density. Here we first show how, by replacing the\nexponential density by a sum of two exponentials, the overlaps statistics are\nmodified. One way to reconcile these results with the replica theory is to\nallow the blocks in the Parisi matrix to fluctuate. Other examples where the\nsizes of these blocks should fluctuate include the finite size corrections of\nthe REM, the case of discrete energies and the overlaps between two\ntemperatures. In all these cases, the blocks sizes not only fluctuate but need\nto take complex values if one wishes to reproduce the results of our\nreplica-free calculations.","PeriodicalId":501066,"journal":{"name":"arXiv - PHYS - Disordered Systems and Neural Networks","volume":"7 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Generalizations of Parisi's replica symmetry breaking and overlaps in random energy models\",\"authors\":\"Bernard Derrida, Peter Mottishaw\",\"doi\":\"arxiv-2408.15125\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The random energy model (REM) is the simplest spin glass model which exhibits\\nreplica symmetry breaking. It is well known since the 80's that its overlaps\\nare non-selfaveraging and that their statistics satisfy the predictions of the\\nreplica theory. All these statistical properties can be understood by\\nconsidering that the low energy levels are the points generated by a Poisson\\nprocess with an exponential density. Here we first show how, by replacing the\\nexponential density by a sum of two exponentials, the overlaps statistics are\\nmodified. One way to reconcile these results with the replica theory is to\\nallow the blocks in the Parisi matrix to fluctuate. Other examples where the\\nsizes of these blocks should fluctuate include the finite size corrections of\\nthe REM, the case of discrete energies and the overlaps between two\\ntemperatures. In all these cases, the blocks sizes not only fluctuate but need\\nto take complex values if one wishes to reproduce the results of our\\nreplica-free calculations.\",\"PeriodicalId\":501066,\"journal\":{\"name\":\"arXiv - PHYS - Disordered Systems and Neural Networks\",\"volume\":\"7 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - PHYS - Disordered Systems and Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2408.15125\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Disordered Systems and Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.15125","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
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。
Generalizations of Parisi's replica symmetry breaking and overlaps in random energy models
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.