Neural Network Reconstruction of Late-Time Cosmology and Null Tests
  The prospect of nonparametric reconstructions of cosmological parameters from
observational data sets has been a popular topic in the literature for a number
of years. This has mainly taken the form of a technique based on Gaussian
processes but this approach is exposed to several foundational issues ranging
from overfitting to kernel consistency problems. In this work, we explore the
possibility of using artificial neural networks (ANN) to reconstruct late-time
expansion and large scale structure cosmological parameters. We first show how
mock data can be used to design an optimal ANN for both parameters, which we
then use with real data to infer their respective redshift profiles. We further
consider cosmological null tests with the reconstructed data in order to
confirm the validity of the concordance model of cosmology, in which we observe
a mild deviation with cosmic growth data.
