Optimizing large-scale structure data analysis with the theoretical error likelihood

Understand

An important aspect of large-scale structure data analysis is the presence of non-negligible theoretical uncertainties, which become increasingly important on small scales.

• We show how to incorporate these uncertainties in realistic power spectrum likelihoods by an appropriate change of the fitting model and the covariance matrix.
• The inclusion of the theoretical error has several advantages over the standard practice of using the sharp momentum cut $k_{\rm max}$.
• First, the theoretical error covariance gradually suppresses the information from the short scales as the employed theoretical model becomes less reliable.