Likelihood-Free Cosmological Inference with Type Ia Supernovae: Approximate Bayesian Computation for a Complete Treatment of Uncertainty

Understand

Cosmological inference becomes increasingly difficult when complex data-generating processes cannot be modeled by simple probability distributions.

• With the ever-increasing size of data sets in cosmology, there is increasing burden placed on adequate modeling; systematic errors in the model will dominate where previously these were swamped by statistical errors.
• For example, Gaussian distributions are an insufficient representation for errors in quantities like photometric redshifts.
• Likewise, it can be difficult to quantify analytically the distribution of errors that are introduced in complex fitting codes.