Neural network reconstruction of cosmology using the Pantheon compilation
Konstantinos F. Dialektopoulos, Purba Mukherjee, Jackson Levi Said, Jurgen Mifsud
Abstract
Abstract In this work, we reconstruct the Hubble diagram using various data sets, including correlated ones, in artificial neural networks (ANN). Using ReFANN, that was built for data sets with independent uncertainties, we expand it to include non-Guassian data points, as well as data sets with covariance matrices among others. Furthermore, we compare our results with the existing ones derived from Gaussian processes and we also perform null tests in order to test the validity of the concordance model of cosmology.
Topics & Concepts
CosmologyArtificial neural networkCovarianceNull (SQL)Computer scienceGaussianAlgorithmNull hypothesisCovariance matrixArtificial intelligenceGaussian processPattern recognition (psychology)Data miningMathematicsStatisticsAstrophysicsPhysicsQuantum mechanicsCosmology and Gravitation TheoriesGalaxies: Formation, Evolution, PhenomenaStatistical and numerical algorithms