Distinguishing standard and modified gravity cosmologies with machine learning

Understand

We present a convolutional neural network to classify distinct cosmological scenarios based on the statistically similar weak-lensing maps they generate.

• Modified gravity (MG) models that include massive neutrinos can mimic the standard concordance model ($\Lambda$CDM) in terms of Gaussian weak-lensing observables.
• An inability to distinguish viable models that are based on different physics potentially limits a deeper understanding of the fundamental nature of cosmic acceleration.
• For a fixed redshift of sources, we demonstrate that a machine learning network trained on simulated convergence maps can discriminate between such models better than conventional higher-order statistics.