Breaking the spell of Gaussianity: forecasting with higher order Fisher matrices

Abstract

We present the new method DALI (Derivative Approximation for LIkelihoods) for reconstructing and forecasting posteriors. DALI extends the Fisher Matrix formalism but allows for a much wider range of posterior shapes. While the Fisher Matrix formalism is limited to yield ellipsoidal confidence contours, our method can reproduce the often observed flexed, deformed or curved shapes of known posteriors. This gain in shape fidelity is obtained by expanding the posterior to higher order in derivatives with respect to parameters, such that non-Gaussianity in the parameter space is taken into account. The resulting expansion is positive definite and normalizable at every order. Here, we present the new technique, highlight its advantages and limitations, and show a representative application to a posterior of dark energy parameters from supernovae measurements.