What does a cosmological experiment really measure? Covariant posterior decomposition with normalizing flows

Understand

We present methods to rigorously extract parameter combinations that are constrained by data from posterior distributions.

• The standard approach uses linear methods that apply to Gaussian distributions.
• We show the limitations of the linear methods for current surveys, and develop non-linear methods that can be used with non-Gaussian distributions, and are independent of the parameter basis.
• These are made possible by the use of machine-learning models, normalizing flows, to learn posterior distributions from their samples.