Quasinormal modes of <i>dS</i> and <i>AdS</i> black holes: Feedforward neural network method

Ali Övgün, İzzet Sakallı, Halil Mutuk

2021International Journal of Geometric Methods in Modern Physics28 citationsDOI Open Access PDF

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: [Formula: see text] 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.

Topics & Concepts

PhysicsSchwarzschild radiusBoundary (topology)Scalar (mathematics)De Sitter universeArtificial neural networkBlack hole (networking)Classical mechanicsMathematical physicsPerturbation (astronomy)Anti-de Sitter spaceScalar fieldMathematical analysisBoundary value problemde Sitter–Schwarzschild metricFeedforward neural networkTheoretical physicsSchwarzschild metricFormalism (music)Statistical physicsBlack Holes and Theoretical PhysicsAstrophysical Phenomena and ObservationsPulsars and Gravitational Waves Research