Neural network reconstructions for the Hubble parameter, growth rate and distance modulus

Abstract

This paper introduces a new approach to reconstruct cosmological functions using artificial neural networks based on observational measurements with minimal theoretical and statistical assumptions. By using neural networks, we can generate computational models of observational datasets, and then we compare them with the original ones to verify the consistency of our method. This methodology is applicable to even small-size datasets. In particular, we test the proposed method with data coming from cosmic chronometers, $f{\sigma }_8$ measurements, and the distance modulus of the Type Ia supernovae. Furthermore, we introduce a first approach to generate synthetic covariance matrices through a variational autoencoder, using the systematic covariance matrix of the Type Ia supernova compilation.

20 pages, 8 figures; matches the version published in EPJC

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• LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applications
• Non-parametric reconstruction of cosmological observables using Gaussian Processes Regression
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• Investigating the Redshift Evolution of Lensing Galaxy Density Slopes via Model-Independent Distance Ratios
• Model-independent calibration of Gamma-Ray Bursts with neural networks
• Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample
• Reconstruction of full sky CMB $\mathbf{E}$ and $\mathbf{B}$ modes spectra removing $\mathbf{E}$-to-$\mathbf{B}$ leakage from partial sky using deep learning
• Cosmological Model Independent Constraints on Lorentz Invariance Violation with Updated Gamma-Ray Burst Observations: An Artificial Neural Network Approach
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• Variational autoencoder for generating realistic $N$-body simulations for dark matter halos