Cosmology with Bayesian Neural Networks

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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.