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

Understand

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.