Cosmological Parameter Estimation and Inference using Deep Summaries

Understand

The ability to obtain reliable point estimates of model parameters is of crucial importance in many fields of physics.

• This is often a difficult task given that the observed data can have a very high number of dimensions.
• In order to address this problem, we propose a novel approach to construct parameter estimators with a quantifiable bias using an order expansion of highly compressed deep summary statistics of the observed data.
• These summary statistics are learned automatically using an information maximising loss.