Quasinormal modes of dS and AdS black holes: Feedforward neural network method

ALI OVGUN, Izzet Sakalll, Halil Mutuk

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we show how the quasinormal modes (QNMs) arise from the perturbations of massive scalar fields propagating in the curved background by using the artificial neural networks. To this end, we architect a special algorithm for the feedforward neural network method (FNNM) to compute the QNMs complying with the certain types of boundary conditions. To test the reliability of the method, we consider two black hole spacetimes whose QNMs are well known: 4D pure de Sitter (dS) and five-dimensional Schwarzschild anti-de Sitter (AdS) black holes. Using the FNNM, the QNMs of are computed numerically. It is shown that the obtained QNMs via the FNNM are in good agreement with their former QNM results resulting from the other methods. Therefore, our method of finding the QNMs can be used for other curved spacetimes that obey the same boundary conditions.

Original languageEnglish
Article number2150154
JournalInternational Journal of Geometric Methods in Modern Physics
DOIs
StateAccepted/In press - 2021
Externally publishedYes

Keywords

  • anti-de Sitter
  • black hole
  • de Sitter
  • feedforward neural network
  • Quasinormal modes

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