Neural network reconstruction of cosmology using the Pantheon compilation

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.

11 pages, 12 sets of figures, Accepted for publication in EPJ C

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Cited by in corpus (13)

• The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics
• Model-independent cosmological inference post DESI DR1 BAO measurements
• $\Lambda$CDM Tensions: Localising Missing Physics through Consistency Checks
• LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applications
• A possible late-time transition of $M_B$ inferred via neural networks
• Dark energy reconstruction analysis with artificial neural networks: Application on simulated Supernova Ia data from Rubin Observatory
• Gamma-Ray Bursts Calibrated from the Observational $H(z)$ Data in Artificial Neural Network Framework
• Probing for Lorentz Invariance Violation in Pantheon Plus Dominated Cosmology
• Statistical Nuances in BAO Analysis: Likelihood Formulations and Non-Gaussianities
• Model-independent calibration of Gamma-Ray Bursts with neural networks
• Testing the Cosmic Distance Duality Relation with Neural Kernel Gaussian Process Regression
• Investigating the Redshift Evolution of Lensing Galaxy Density Slopes via Model-Independent Distance Ratios
• Reconstructing the Type Ia Supernova Absolute Magnitude with Two-Probe Physics-Informed Neural Networks