Speaker
Description
In this talk I will introduce a new paradigm for cosmological inference, enabled by recent advances in machine learning and its underlying technology. By combining emulation, differentiable and probabilistic programming, scalable gradient-based sampling, and decoupled Bayesian model selection, this framework scales to extremely high-dimensional parameter spaces and enables complete Bayesian analyses—encompassing both parameter estimation and model selection—in a fraction of the time required by conventional approaches. I will demonstrate its application to various Stage IV cosmological survey configurations, tackling parameter spaces of approximately 150 dimensions that are inaccessible to standard techniques. I will also show how this framework can be used to test competing gravity theories. Finally, I will illustrate how a field-level analysis of Euclid cosmic shear data could definitively confirm or refute the recent DESI results pointing to dynamical dark energy.
| AI keywords | simulation-based inference, surrogate modelling, hardware acceleration, differentiable programming, probabilistic programming |
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