Mancini, A Spurio; Pourtsidou, A; (2022) KiDS-1000 cosmology: machine learning – accelerated constraints on interacting dark energy with COSMOPOWER. Monthly Notices of the Royal Astronomical Society: Letters , 512 (1) L44-L48. 10.1093/mnrasl/slac019.

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Abstract

We derive constraints on a coupled quintessence model with pure momentum exchange from the public ∼1000 deg2 cosmic shear measurements from the Kilo-Degree Survey and the Planck 2018 cosmic microwave background data. We compare this model with Lambda cold dark matter and find similar χ2 and log-evidence values. We accelerate parameter estimation by sourcing cosmological power spectra from the neural network emulator COSMOPOWER. We highlight the necessity of such emulator-based approaches to reduce the computational runtime of future similar analyses, particularly from Stage IV surveys. As an example, we present Markov Chain Monte Carlo forecasts on the same coupled quintessence model for a Euclid-like survey, revealing degeneracies between the coupled quintessence parameters and the baryonic feedback and intrinsic alignment parameters, but also highlighting the large increase in constraining power Stage IV surveys will achieve. The contours are obtained in a few hours with COSMOPOWER, as opposed to the few months required with a Boltzmann code.

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