Hans Kristian EriksenView profile
Professor
Professor Hans Kristian Eriksen is a leading cosmologist at the Institute of Theoretical Astrophysics, University of Oslo, specializing in primordial gravitational wave detection through cosmic microwave background (CMB) analysis. His work bridges advanced computational methods with high-precision observational cosmology, focusing on next-generation satellite missions like LiteBIRD and ground-based experiments including COMAP and SPIDER. His research centers on developing exascale computational frameworks for Bayesian end-to-end CMB analysis, with particular emphasis on extracting faint primordial B-mode signals from overwhelming foreground contamination. Key methodologies include Monte Carlo Markov Chain samplers, component separation techniques, and systematic error propagation models that enable unprecedented precision in cosmological parameter estimation. Recent publications demonstrate dominant focus on the LiteBIRD satellite mission across simulation frameworks, scanning optimization, and science forecasts, alongside significant contributions to CO intensity mapping through the COMAP project. These works reveal evolving trends toward multi-messenger cosmology and increasingly sophisticated foreground mitigation strategies. ERC Starting Grant ERC Consolidator Grant H2020 COMPET-4 project (BeyondPlanck) Eriksen serves as principal investigator for major computational cosmology projects, including the Cosmoglobe initiative for end-to-end CMB analysis. His supervisory role encompasses computational astrophysics projects focused on massive parallelization of CMB analysis codes, with funding secured through competitive European grants. His team maintains active collaborations with NASA/JPL, Caltech, Oxford, and international consortia. He leads the Cosmoglobe group implementing one of the world's most comprehensive cosmological MCMC samplers, while actively contributing to the COMAP, PASIPHAE, Planck, QUIET, and SPIDER collaborations. Current efforts concentrate on preparing computational pipelines for LiteBIRD data analysis and advancing Bayesian methods for next-generation CMB polarization experiments.


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