Martin Rodríguez Monroy

ESA's Euclid and the Vera C. Rubin Observatory's LSST Stage-IV surveys, will revolutionise cosmology with unprecedented data. However, systematic effects, particularly spatially varying observational systematics (SVOS) in galaxy clustering, now dominate uncertainties, threatening to bias key cosmological parameters. We will tackle this challenge with a novel analysis, developing a unified mathematical and computing framework for SVOS characterisation and correction. Using explainable AI, we will create a pipeline to study biases in cosmological parameter space, providing vital tools to obtain unbiased estimates and ultimately reveal dark energy's nature.