Extending BEAMS to incorporate correlated systematic uncertainties

TL;DR

This paper extends the BEAMS formalism to handle correlated systematic uncertainties in photometric supernova surveys, enabling unbiased cosmological parameter estimation despite large, unknown systematics.

Contribution

The authors develop a numerical marginalisation method to incorporate correlated systematics into BEAMS, avoiding the exponential computational complexity.

Findings

Numerical marginalisation effectively manages large systematic uncertainties.

Ignoring correlations can cause significant biases in cosmological parameters.

The perturbative expansion introduces biases due to misclassification.

Abstract

New supernova surveys such as the Dark Energy Survey, Pan-STARRS and the LSST will produce an unprecedented number of photometric supernova candidates, most with no spectroscopic data. Avoiding biases in cosmological parameters due to the resulting inevitable contamination from non-Ia supernovae can be achieved with the BEAMS formalism, allowing for fully photometric supernova cosmology studies. Here we extend BEAMS to deal with the case in which the supernovae are correlated by systematic uncertainties. The analytical form of the full BEAMS posterior requires evaluating 2^N terms, where N is the number of supernova candidates. This `exponential catastrophe' is computationally unfeasible even for N of order 100. We circumvent the exponential catastrophe by marginalising numerically instead of analytically over the possible supernova types: we augment the cosmological parameters with nuisance parameters describing the covariance matrix and the types of all the supernovae, \tau_i, that we include in our MCMC analysis. We show that this method deals well even with large, unknown systematic uncertainties without a major increase in computational time, whereas ignoring the correlations can lead to significant biases and incorrect credible contours. We then compare the numerical marginalisation technique with a perturbative expansion of the posterior based on the insight that future surveys will have exquisite light curves and hence the probability that a given candidate is a Type Ia will be close to unity or zero, for most objects. Although this perturbative approach changes computation of the posterior from a 2^N problem into an N^2 or N^3 one, we show that it leads to biases in general through a small number of misclassifications, implying that numerical marginalisation is superior.