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arXiv 2201.04250hep-thcond-mat.dis-nn
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信号检测的泛函重整化群方法

Functional Renormalization Group Approach for Signal Detection

Université Paris Saclay(巴黎萨克雷大学);Cea(法国原子能和替代能源委员会);List(列日研究所);Faculté des Sciences et Techniques (ICMPA-UNESCO Chair) Université d’Abomey-Calavi(阿博米-卡拉维大学科学与技术学院(ICMPA-UNESCO讲席))

Vincent Lahoche, Dine Ousmane Samary, Mohamed Tamaazousti

AI总结:

利用重整化群技术处理近连续正谱信号检测,构建数据有效场论框架,揭示检测阈值附近的相变与对称破缺的普适关系。

摘要:

这篇综述论文使用重整化群技术进行近连续正谱中的信号检测。我们强调了模拟场论方法的普适性方面。第一个目标是提出一个扩展的自洽构建数据模拟有效场论框架,可视为最大熵模型。特别地,利用普适性论证,我们证明了经典作用的$\mathbb{Z}_2$对称性,并强调存在大尺度(局域)区域和小尺度(非局域)区域。其次,与噪声模型相关,我们观察到检测阈值附近相变与对称破缺之间的普适关系。最后,我们讨论了为张量类数据定义协方差矩阵的问题。基于切割图规则,我们注意到基于大尺寸完全图的定义在数据分析中的优越性。

英文摘要:

This review paper uses renormalization group techniques for signal detection in nearly-continuous positive spectra. We highlight universal aspects of the analogue field-theory approach. The first aim is to present an extended self-consistent construction of the analogue effective field-theory framework for data, which can be viewed as a maximum entropy model. In particular and exploiting universality arguments, we justify the $\mathbb{Z}_2$-symmetry of the classical action and we stress the existence of a large-scale (local) regime and of a small-scale (nonlocal) regime. Secondly and related to noise models, we observe the universal relation between phase transition and symmetry breaking in the vicinity of the detection threshold. Finally, we discuss the issue of defining the covariance matrix for tensorial-like data. Based on the cutting graph prescription, we note the superiority of definitions based on complete graphs of large size for data analysis.

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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