# Quasinormal modes of dS and AdS Black Holes: Feedforward neural network method

## TL;DR

This paper introduces a neural network-based algorithm to compute quasinormal modes of black holes, demonstrating its accuracy on known spacetimes and suggesting its applicability to other curved backgrounds.

## Contribution

The paper develops a specialized feedforward neural network method for calculating QNMs with boundary conditions, validated against known black hole solutions.

## Key findings

- FNNM accurately reproduces known QNMs for dS and AdS black holes.
- The method shows good agreement with traditional QNM calculation techniques.
- FNNM can be extended to other curved spacetimes with similar boundary conditions.

## 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.