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2021
DOI: 10.1142/s0219887821501541
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Quasinormal modes of dS and AdS black holes: Feedforward neural network method

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-di… Show more

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Cited by 19 publications
(8 citation statements)
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References 113 publications
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“…Mathematically, the artificial neural network is an optimization scheme that can be adopted to solve eigenvalue problems [87]. In [88], the method was adopted for black hole QNMs and exercised for four-dimensional pure dS and five-dimensional Schwarzschild AdS black holes. Good agreement was manifestly obtained when compared with other methods.…”
Section: Further Discussion and Concluding Remarksmentioning
confidence: 99%
“…Mathematically, the artificial neural network is an optimization scheme that can be adopted to solve eigenvalue problems [87]. In [88], the method was adopted for black hole QNMs and exercised for four-dimensional pure dS and five-dimensional Schwarzschild AdS black holes. Good agreement was manifestly obtained when compared with other methods.…”
Section: Further Discussion and Concluding Remarksmentioning
confidence: 99%
“…Feedforward neural network method [100,101]. On the other hand, the method that we will mainly focus on in this review will be the WKB approximation.…”
Section: Discussionmentioning
confidence: 99%
“…Consequently, other alternative approaches have been proposed for calculating QNM frequencies, including classical and numerical methods such as the Chandrasekhar-Detweiler method, direct integration of the wave equation, the Frobenius series method and its variations, the continued fractions method, and the monodromy technique for highly damped QNMs (for a discussion of these methods see [2,53]). In recent years, novel and alternative computational methods have also been developed to obtain BH QNM frequencies, such as the Borel summation method [54], the Jansen Mathematica package [55] or the use of Neural Networks Methods [56].…”
Section: The Pöschl-teller Potential Methodsmentioning
confidence: 99%