Cosmology with Bayesian Neural Networks

The nature of Dark Energy and Dark Matter have been puzzling the cosmological community for decades. Upcoming stage IV galaxy surveys like Euclid will provide detailed data on non-linear structures. We will use this new information to constrain dark energy and modified gravity theories.

We are developing a Bayesian Neural Network that uses the matter power spectrum including the non-linear modes to deduce the theory of gravity that is most likely to govern the underlying universe.

Later on we are going to develop a similar classification technique for observables that can be obtained from Euclid data.