Advancing Field-level and Simulation-based Inference for Cosmology

Enabling KARMMA as a Tool of Precision Cosmology

Jun 8, 2026, 3:40 PM

20m

PI/4-405 - Bob Room (Perimeter Institute for Theoretical Physics)

Contributed Talk

Speaker

Ivan Espinoza Bustamante (The University of Arizona)

Description

We will present KARMMA, a field-level inference designed to enable joint inference of cosmology and the convergence field from cosmic shear data. KARMMA is a full-sky Bayesian algorithm that forward-models the convergence field as an augmented lognormal realization. Using N-body simulations, we generate mock cosmic shear observations and use these to validate the cosmological inferences from KARMMA. We find field-level inference with LSST Y1-like data has the potential to improve constraints on the dark energy equation of state by a factor of six relative to a standard power-spectrum approach.

Presentation materials

kamma_talk_perimeter_slides.pptx

Talk Recording (26060021)

External references

• 26060021
• 9cc863dc-2fff-4e49-8814-4379e03bbc15