2021/12/31 by Juan de Dios Rojas Olvera, Isidro Gómez-Vargas, J. Alberto Vázquez
Computer Science · Physics and Astronomy · #Artificial intelligence #Artificial neural network #Astrophysics #Computational Physics and Python Applications #Computer science #Cosmology #Cosmology and Gravitation Theories #Galaxies: Formation, Evolution, Phenomena #Machine learning #Nervous system network models #Physics #Recurrent neural network #Types of artificial neural networks #astro-ph.CO #astro-ph.IM
paper · pdf · doi:10.3390/universe8020120
published as Universe 2022, 8(2), 120 · 17 pages, 13 figures; matches the version published in Universe
openalex publication_date 2022/02/12 · arxiv created 2022/02/14 · arxiv updated 2022/02/15 · openalex created_date 2022/02/24 · openalex updated_date 2026/08/05
In cosmology, the analysis of observational evidence is very important when testing theoretical models of the Universe. Artificial neural networks are powerful and versatile computational tools for data modelling and have recently been considered in the analysis of cosmological data. The main goal of this paper is to provide an introduction to artificial neural networks and to describe some of their applications to cosmology. We present an overview on the fundamentals of neural networks and their technical details. Through three examples, we show their capabilities in the modelling of cosmological data, numerical tasks (saving computational time), and the classification of stellar objects. Artificial neural networks offer interesting qualities that make them viable alternatives for data analysis in cosmological research.