Busillo, Valerio (2025) Scalable strong-lens science with Euclid: detection, modelling, and scientific exploitation. [Tesi di dottorato]

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Abstract

The nature of dark matter and dark energy, the dominant components of our Universe, remains one of the most profound mysteries in modern cosmology. Strong gravitational lensing, a direct consequence of Einstein’s General Relativity, provides a uniquely powerful probe of the mass distribution in galaxies and the fundamental physical processes that govern their evolution. The European Space Agency’s Euclid mission is poised to revolutionize this field by discovering an unprecedented sample of over 10^5 new galaxy-galaxy strong lenses, offering an enormous amount of data to explore the dark Universe. However, the sheer volume and complexity of data to be produced by such large-scale surveys makes traditional analysis methods insufficient. This thesis confronts this challenge by developing and validating a complete pipeline for the automated detection, modelling, and scientific exploitation of strong gravitational lenses. By using neural networks fine-tuned to handle Euclid observations, this work demonstrates both a significant acceleration of the modelling process and a potential synergy with standard modelling techniques, enabling the analysis of strong lenses on a scale never before possible. Beyond tool development, this thesis introduces a novel procedure for exploiting galaxy observables derivable from lensing by systematically comparing galaxy scaling relations with large suites of hydrodynamical cosmological simulations. By developing both automated tools and innovative analysis techniques, essential for the era of large-scale surveys, this thesis lays the groundwork to fully harness the scientific potential of the Euclid mission.

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