Orateur
Description
The nature of dark energy remains one of the most profound open questions in modern cosmology. Despite two decades of increasingly precise observations, the standard cosmological model—anchored by a cosmological constant—continues to provide an excellent phenomenological fit, while raising deep theoretical tensions. As we enter the era of high-precision large-scale structure surveys, led by missions such as Euclid, we are poised to move beyond parameter estimation into a regime of model discrimination and discovery.
In this talk, I will discuss the next frontier in dark energy research from both theoretical and data-driven perspectives. I will highlight how advances in modeling of large-scale structure observables, combined with next-generation inference pipelines, are reshaping our ability to test extensions beyond ΛCDM. Emphasis will be placed on the role of effective field theory approaches, novel observables, and the integration of machine learning techniques to accelerate Bayesian inference and capture non-linear structure formation.
I will present recent developments in the construction and validation of end-to-end cosmological pipelines within the Euclid Science Ground Segment, and discuss how upcoming data releases will enable stringent constraints on dynamical dark energy, modified gravity, and beyond-standard paradigms. Finally, I will outline the key theoretical and computational challenges that must be addressed to fully exploit this new data landscape, and argue that the coming decade will be defined not only by precision cosmology, but by our ability to robustly interpret it.