重访查克-科丁顿模型的实空间重正化方法:改进的统计数据

Syl Shaw, Rudolf A. Römer
{"title":"重访查克-科丁顿模型的实空间重正化方法:改进的统计数据","authors":"Syl Shaw, Rudolf A. Römer","doi":"arxiv-2404.00660","DOIUrl":null,"url":null,"abstract":"The real-space renormalisation group method can be applied to the\nChalker-Coddington model of the quantum Hall transition to provide a convenient\nnumerical estimation of the localisation critical exponent, $\\nu$. Previous\nsuch studies found $\\nu\\sim 2.39$ which falls considerably short of the current\nbest estimates by transfer matrix ($\\nu\\approx 2.593$) and\nexact-diagonalisation studies ($\\nu=2.58(3)$). By increasing the amount of data\n$500$ fold we can now measure closer to the critical point and find an improved\nestimate $\\nu\\approx 2.51$. This deviates only $\\sim 3\\%$ from the previous two\nvalues and is already better than the $\\sim 7\\%$ accuracy of the classical\nsmall-cell renormalisation approach from which our method is adapted. We also\nstudy a previously proposed mixing of the Chalker-Coddington model with a\nclassical scattering model which is meant to provide a route to understanding\nwhy experimental estimates give a lower $\\nu\\sim 2.3$. Upon implementing this\nmixing into our RG unit, we find only further increases to the value of $\\nu$.","PeriodicalId":501066,"journal":{"name":"arXiv - PHYS - Disordered Systems and Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Real-space renormalisation approach to the Chalker-Coddington model revisited: improved statistics\",\"authors\":\"Syl Shaw, Rudolf A. Römer\",\"doi\":\"arxiv-2404.00660\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The real-space renormalisation group method can be applied to the\\nChalker-Coddington model of the quantum Hall transition to provide a convenient\\nnumerical estimation of the localisation critical exponent, $\\\\nu$. Previous\\nsuch studies found $\\\\nu\\\\sim 2.39$ which falls considerably short of the current\\nbest estimates by transfer matrix ($\\\\nu\\\\approx 2.593$) and\\nexact-diagonalisation studies ($\\\\nu=2.58(3)$). By increasing the amount of data\\n$500$ fold we can now measure closer to the critical point and find an improved\\nestimate $\\\\nu\\\\approx 2.51$. This deviates only $\\\\sim 3\\\\%$ from the previous two\\nvalues and is already better than the $\\\\sim 7\\\\%$ accuracy of the classical\\nsmall-cell renormalisation approach from which our method is adapted. We also\\nstudy a previously proposed mixing of the Chalker-Coddington model with a\\nclassical scattering model which is meant to provide a route to understanding\\nwhy experimental estimates give a lower $\\\\nu\\\\sim 2.3$. Upon implementing this\\nmixing into our RG unit, we find only further increases to the value of $\\\\nu$.\",\"PeriodicalId\":501066,\"journal\":{\"name\":\"arXiv - PHYS - Disordered Systems and Neural Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-31\",\"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-2404.00660\",\"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-2404.00660","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

实空间重正化群方法可以应用于量子霍尔转换的查克-科丁顿模型,从而对局域化临界指数($\nu$)进行方便的数值估计。之前的研究发现$\nu\sim 2.39$大大低于目前通过转移矩阵($\nu\approx 2.593$)和精确对角研究($\nu=2.58(3)$)得出的最佳估计值。通过增加500倍的数据量,我们现在可以测量到更接近临界点的数据,并找到一个改进的估计值 $\nu\approx 2.51$。这与之前的两个数值仅相差 $\sim 3\%$ ,已经优于经典小室重正化方法的精度 $\sim 7\%$ ,而我们的方法正是从经典小室重正化方法改编而来的。我们还研究了之前提出的将查克-科丁顿模型与经典散射模型混合的方法,其目的是为理解为什么实验估计值会低于2.3美元提供一条途径。在我们的RG单元中实施这种混合后,我们发现$\nu$的值只会进一步增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-space renormalisation approach to the Chalker-Coddington model revisited: improved statistics
The real-space renormalisation group method can be applied to the Chalker-Coddington model of the quantum Hall transition to provide a convenient numerical estimation of the localisation critical exponent, $\nu$. Previous such studies found $\nu\sim 2.39$ which falls considerably short of the current best estimates by transfer matrix ($\nu\approx 2.593$) and exact-diagonalisation studies ($\nu=2.58(3)$). By increasing the amount of data $500$ fold we can now measure closer to the critical point and find an improved estimate $\nu\approx 2.51$. This deviates only $\sim 3\%$ from the previous two values and is already better than the $\sim 7\%$ accuracy of the classical small-cell renormalisation approach from which our method is adapted. We also study a previously proposed mixing of the Chalker-Coddington model with a classical scattering model which is meant to provide a route to understanding why experimental estimates give a lower $\nu\sim 2.3$. Upon implementing this mixing into our RG unit, we find only further increases to the value of $\nu$.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信