Quantifying systematic uncertainties in supernova cosmology

TL;DR

This paper introduces SMOCK, a Monte Carlo simulation method to quantify how various systematic uncertainties affect supernova-based measurements of the Universe's expansion, highlighting potential impacts on dark energy estimates.

Contribution

The paper presents a novel iterative Monte Carlo technique (SMOCK) for assessing systematic errors in supernova cosmology, especially non-Gaussian and correlated effects.

Findings

Systematic uncertainties can surpass statistical errors in supernova data.

SMOCK effectively tracks impact of errors like reddening, brightness evolution, and lensing.

Potential biases in dark energy parameters are identified.

Abstract

Observations of Type Ia supernovae used to map the expansion history of the Universe suffer from systematic uncertainties that need to be propagated into the estimates of cosmological parameters. We propose an iterative Monte-Carlo simulation and cosmology fitting technique (SMOCK) to investigate the impact of sources of error upon fits of the dark energy equation of state. This approach is especially useful to track the impact of non-Gaussian, correlated effects, e.g. reddening correction errors, brightness evolution of the supernovae, K-corrections, gravitational lensing, etc. While the tool is primarily aimed for studies and optimization of future instruments, we use the ``Gold'' data-set in Riess et al. (2007) to show examples of potential systematic uncertainties that could exceed the quoted statistical uncertainties.