Like vs. Like: Strategy and Improvements in Supernova Cosmology Systematics

TL;DR

This paper discusses strategies for minimizing systematic uncertainties in supernova cosmology by using empirically defined like supernova subsets to improve the accuracy of dark energy measurements.

Contribution

It introduces a method for selecting supernova subsets based on observational properties to reduce bias and uncertainty in cosmological parameters.

Findings

Neglecting like vs. like comparison biases dark energy constraints by 1σ.

Proper subset recognition reduces bias and degradation significantly.

Observational accuracy at 0.016 mag level is crucial for effective subset classification.

Abstract

Control of systematic uncertainties in the use of Type Ia supernovae as standardized distance indicators can be achieved through contrasting subsets of observationally-characterized, like supernovae. Essentially, like supernovae at different redshifts reveal the cosmology, and differing supernovae at the same redshift reveal systematics, including evolution not already corrected for by the standardization. Here we examine the strategy for use of empirically defined subsets to minimize the cosmological parameter risk, the quadratic sum of the parameter uncertainty and systematic bias. We investigate the optimal recognition of subsets within the sample and discuss some issues of observational requirements on accurately measuring subset properties. Neglecting like vs. like comparison (i.e. creating only a single Hubble diagram) can cause cosmological constraints on dark energy to be biased by 1\sigma or degraded by a factor 1.6 for a total drift of 0.02 mag. Recognition of subsets at the 0.016 mag level (relative differences) erases bias and reduces the degradation to 2%.