Optimal Cosmological Analysis at the Pixel level

forthcoming

Distorted by the gravity of cosmic structures, galaxy shapes can answer the biggest questions in cosmology: How do cosmic structures form? What is the nature of dark energy and dark matter? While the cosmological model succeeds at explaining a wide range of observations, it relies on two components whose nature remains unknown: dark matter and dark energy. Moreover, weak lensing surveys have found that the Universe is more homogeneous than expected from the cosmic microwave background, indicating a tension between observations. Resolving this tension and understanding the dark components of the Universe are the most pressing challenges of cosmology today. The Euclid mission and the Rubin Observatory will observe the positions and shapes of billions of galaxies with unprecedented precision. These data have the potential to revolutionise our understanding of dark energy and detect deviations from the cosmological model. However, standard analyses discard large amounts of information in the data. To realise the full potential of these surveys, we urgently need more advanced data analysis techniques. The OCAPi project will develop cutting-edge methods to make optimal use of these surveys, delivering the most precise constraints on the cosmological parameters from Euclid data. By analysing lensing maps at the pixel level, OCAPi will capture all the information in the data and increase the precision of the cosmological constraints, yielding marginal errors up to a factor 5 better than standard techniques. OCAPi will move this innovative approach from its proof-of-concept to the full application to Euclid data to investigate the cosmology tension, constrain dark energy models and generate probabilistic mass maps of the Universe. Making optimal use of one of the most powerful cosmological datasets, OCAPi will advance our understanding of the dark components of the Universe and the formation of cosmic structures, setting new benchmarks for cosmological analysis.