PIRSA:26060021 Conference Cosmology

DOI10.48660/26060021

Conference/School: Advancing Field-level and Simulation-based Inference for Cosmology

Espinoza Bustamante, I. (2026). Enabling KARMMA as a Tool of Precision Cosmology. Perimeter Institute. https://pirsa.org/26060021

Espinoza Bustamante, Ivan. Enabling KARMMA as a Tool of Precision Cosmology. Perimeter Institute, Jun. 08, 2026, https://pirsa.org/26060021 

@misc{ pirsa_PIRSA:26060021,
  doi = {10.48660/26060021},
  url = {https://pirsa.org/26060021},
  author = {Espinoza Bustamante, Ivan},
  keywords = {Cosmology},
  language = {en},
  title = {Enabling KARMMA as a Tool of Precision Cosmology},
  publisher = {Perimeter Institute},
  year = {2026},
  month = {jun},
  note = {PIRSA:26060021 see, \url{https://pirsa.org}}
}

Abstract

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.

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