Título: Mass Determination of Black Holes from Gravitational Wave Data Using Neural Networks
Autores: Blenda Úlima Rodrigues Cesar Guedes, Antônio dos Santos Pádua and Tiago Alessandro Espínola Ferreira.
Resumo:
Gravitational waves were predicted by Albert Einstein more than a century ago, but only in 2015, the Laser Interferometer Gravitational-Wave Observatory (LIGO) was able to detect them. The gravitational wave phenomenon can be compared to spreading water from a lake after a stone has been thrown into it. Here, gravitational wave generation comes from an astronomical binary system formed by black holes or neutron stars. However, unlike the water, the amplitude of those gravitational waves is on a scale smaller than a proton’s size. Despite this, we can describe it with simple equations in a phenomenological way. We can model those waves on a regular computer using post Newtonian physics. Here we were able to generate gravitational waves from computational simulations and make its data analyze. When a gravitational wave is detected in the real-world problem, there is a great interest in establishing the physical features of the astronomical bodies involved in the process. In this way, we propose applying a simple neural network to receive the gravitational wave data and infer information about the astronomical bodies’ mass. The experimental results show that a simple neural network can extract mass information from the gravitational wave data. The recognition process proposed is much more straightforward than the complex computation based on numerical relativity for gravitational wave data analysis.
Palavras-chave:
Neural Networks, gravitational waves, machine learning, relativity.
Páginas: 6
Código DOI: 10.21528/CBIC2021-91
Artigo em pdf: CBIC_2021_paper_91.pdf
Arquivo BibTeX: CBIC_2021_91.bib