Speaker
Description
Cosmology is entering an era in which inference can be performed directly from maps and fields, using simulation-based inference (SBI) across both large-scale structure and CMB surveys, offering a way to trace the underlying rhythms of the cosmos across multiple probes and scales.
I will begin by motivating field-level inference as a powerful approach to extracting cosmological information from cosmic structure and reconstructing the initial conditions of the Universe. I will present and benchmark a range of field-level methods, from differentiable forward modeling to machine-learning approaches, and show the significant gains they deliver for DESI BAO. I will then present an SBI pipeline for large-scale CMB B modes in current ground-based experiments, showing how combining Needlet-ILC with neural posterior estimation allows us to marginalize over complex Galactic foregrounds while improving constraints on the tensor-to-scalar ratio, r.
I will conclude by presenting methods to make SBI robust, interpretable, and efficient, and by outlining its broader role in next-generation cosmology, including simulations being developed within the Simons Observatory ecosystem to enable SBI for multi-probe cross-correlations.