論文

査読有り 本文へのリンクあり
2018年2月22日

Deep Learning and AdS/CFT

Phys. Rev. D 98, 046019 (2018)
  • Koji Hashimoto
  • ,
  • Sotaro Sugishita
  • ,
  • Akinori Tanaka
  • ,
  • Akio Tomiya

記述言語
掲載種別
研究論文(学術雑誌)
DOI
10.1103/PhysRevD.98.046019

We present a deep neural network representation of the AdS/CFT<br />
correspondence, and demonstrate the emergence of the bulk metric function via<br />
the learning process for given data sets of response in boundary quantum field<br />
theories. The emergent radial direction of the bulk is identified with the<br />
depth of the layers, and the network itself is interpreted as a bulk geometry.<br />
Our network provides a data-driven holographic modeling of strongly coupled<br />
systems. With a scalar $\phi^4$ theory with unknown mass and coupling, in<br />
unknown curved spacetime with a black hole horizon, we demonstrate our deep<br />
learning (DL) framework can determine them which fit given response data.<br />
First, we show that, from boundary data generated by the AdS Schwarzschild<br />
spacetime, our network can reproduce the metric. Second, we demonstrate that<br />
our network with experimental data as an input can determine the bulk metric,<br />
the mass and the quadratic coupling of the holographic model. As an example we<br />
use the experimental data of magnetic response of a strongly correlated<br />
material Sm$_{0.6}$Sr$_{0.4}$MnO$_3$. This AdS/DL correspondence not only<br />
enables gravity modeling of strongly correlated systems, but also sheds light<br />
on a hidden mechanism of the emerging space in both AdS and DL.

リンク情報
DOI
https://doi.org/10.1103/PhysRevD.98.046019
arXiv
http://arxiv.org/abs/arXiv:1802.08313
Arxiv Url
http://arxiv.org/abs/1802.08313v1
Arxiv Url
http://arxiv.org/pdf/1802.08313v1 本文へのリンクあり
ID情報
  • DOI : 10.1103/PhysRevD.98.046019
  • arXiv ID : arXiv:1802.08313

エクスポート
BibTeX RIS