An empirical approach to model selection: weak lensing and intrinsic alignments

Understand

In cosmology, we routinely choose between models to describe our data, and can incur biases due to insufficient models or lose constraining power with overly complex models.

• In this paper we propose an empirical approach to model selection that explicitly balances parameter bias against model complexity.
• Our method uses synthetic data to calibrate the relation between bias and the $\chi^2$ difference between models.
• This allows us to interpret $\chi^2$ values obtained from real data (even if catalogues are blinded) and choose a model accordingly.