Speaker
Benjamin Joachimi
(University College London)
Description
Simulation-based inference (SBI) building on machine-learnt density estimation and massive data compression has the potential to become the method of choice for analysing large, complex datasets in survey cosmology. I will present recent work that implements every ingredient of the current Kilo-Degree Survey weak lensing analysis into an SBI framework which runs on similar timescales as a traditional analysis relying on analytic models and a Gaussian likelihood. We show how the SBI analysis recovers and, in several key aspects, goes beyond the traditional approach. I will also discuss challenges and their solutions to SBI-related data compression and goodness-of-fit in several real-world cosmology applications.
Primary author
Benjamin Joachimi
(University College London)
