Gravitational lensing is one of the major tools to address the long-standing problems in astrophysics and cosmology, such as dark energy, dark matter, and black hole physics. Progress has so far been limited by the small number of known lensed systems and the intricate nature of the phenomenon, requiring painstaking handcrafted modelling approaches, hard-to-procure high-quality data, and lengthy computations. Upcoming all-sky surveys will revolutionize the field by discovering orders-of-magnitude more lenses and providing adequate information to understand them in depth. Mining these massive troves of data for the new astrophysical discoveries they hold requires a paradigm shift in our analysis approaches, providing high flexibility, automation, and adaptability to a much larger parameter space, in order to determine critical lensing and physical parameters. GLADIUS has the potential to spearhead gravitational lensing science with the next generation of observations by: i) increasing the accuracy and flexibility of the time delay method to measure cosmic expansion ii) disentangling baryonic and dark matter in galaxies through lensing self-consistently and iii) providing accurate predictions of those rare microlensing events, rich in information on quasar structure and black hole environments. To this end, existing state-of-the-art traditional modelling approaches and simulations will be fused with the groundbreaking machine learning framework of Deep Learning and extended through powerful methodologies, like supervised and generative learning, dramatically increasing the scope of gravitational lensing studies. The resulting data-intensive framework will be directly applicable to the high-resolution imaging and monitoring data products of the upcoming Euclid and LSST surveys. GLADIUS is perfectly aligned with the MSCA scope, allowing me to combine my lens modelling expertise with the leading role of EPFL in major global observational and modelling projects.
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