Skip to main content
CenXiv.org
This website is in trial operation, support us!
We gratefully acknowledge support from all contributors.
Contribute
Donate
cenxiv logo > hep-th > arXiv:1702.01738

Help | Advanced Search

High Energy Physics - Theory

arXiv:1702.01738 (hep-th)
[Submitted on 6 Feb 2017 (v1) , last revised 25 May 2020 (this version, v5)]

Title: Supersymmetric SYK model and random matrix theory

Title: 超对称SYK模型和随机矩阵理论

Authors:Tianlin Li, Junyu Liu, Yuan Xin, Yehao Zhou
Abstract: In this paper, we investigate the effect of supersymmetry on the symmetry classification of random matrix theory ensembles. We mainly consider the random matrix behaviors in the $\mathcal{N}=1$ supersymmetric generalization of the Sachdev-Ye-Kitaev (SYK) model, a toy model for the two-dimensional quantum black hole with supersymmetric constraint. Some analytical arguments and numerical results are given to show that the statistics of the supersymmetric SYK model could be interpreted as random matrix theory ensembles, with a different eight-fold classification from the original SYK model and some new features. The time-dependent evolution of the spectral form factor is also investigated, where predictions from random matrix theory are governing the late time behavior of the chaotic Hamiltonian with supersymmetry.
Abstract: 本文研究了超对称性对随机矩阵理论系综对称分类的影响。我们主要关注 Sachdev-Ye-Kitaev (SYK) 模型的超对称推广(即$\mathcal{N}=1$)中随机矩阵的行为,该模型是带有超对称约束的二维量子黑洞的玩具模型。通过一些解析论证和数值结果表明,超对称SYK模型的统计特性可以被解释为随机矩阵理论系综,并且与原始SYK模型具有不同的八重分类以及一些新特征。此外,还研究了谱函数因子的时间演化,其中随机矩阵理论的预测控制着具有超对称性的混沌哈密顿量的晚期行为。
Comments: Published version; Further revisions about statements and presentations
Subjects: High Energy Physics - Theory (hep-th) ; Statistical Mechanics (cond-mat.stat-mech); Quantum Physics (quant-ph)
Cite as: arXiv:1702.01738 [hep-th]
  (or arXiv:1702.01738v5 [hep-th] for this version)
  https://doi.org/10.48550/arXiv.1702.01738
arXiv-issued DOI via DataCite
Journal reference: JHEP 1706 (2017) 111
Related DOI: https://doi.org/10.1007/JHEP06%282017%29111
DOI(s) linking to related resources

Submission history

From: Junyu Liu [view email]
[v1] Mon, 6 Feb 2017 18:48:34 UTC (1,099 KB)
[v2] Tue, 14 Feb 2017 23:57:47 UTC (1,098 KB)
[v3] Mon, 5 Jun 2017 18:27:08 UTC (1,268 KB)
[v4] Sat, 24 Jun 2017 04:57:38 UTC (1,268 KB)
[v5] Mon, 25 May 2020 15:58:31 UTC (1,540 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled
  • View Chinese PDF
  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
hep-th
< prev   |   next >
new | recent | 2017-02
Change to browse by:
cond-mat
cond-mat.stat-mech
quant-ph

References & Citations

  • NASA ADS
  • Google Scholar
  • Semantic Scholar
a export BibTeX citation Loading...

BibTeX formatted citation

×
Data provided by:

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
IArxiv Recommender (What is IArxiv?)
  • Author
  • Venue
  • Institution
  • Topic

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
  • About
  • Help
  • contact arXivClick here to contact arXiv Contact
  • subscribe to arXiv mailingsClick here to subscribe Subscribe
  • Copyright
  • Privacy Policy
  • Web Accessibility Assistance
  • arXiv Operational Status
    Get status notifications via email or slack

京ICP备2025123034号