The Self-Calibrating Hubble Diagram

TL;DR

This paper introduces a self-calibrating method for the Hubble diagram using SNe Ia data, which reduces systematic uncertainties by including reference spectrum perturbations as nuisance parameters, improving dark energy measurements.

Contribution

It presents the first application of a self-calibrating approach to real supernova data, demonstrating its potential to reduce systematic uncertainties in cosmological measurements.

Findings

Perturbations of the reference spectrum are consistent with zero at 1% level.

Approximately 3500 supernovae are needed for the method to outperform standard techniques.

The method effectively incorporates systematic uncertainty reduction into cosmological fits.

Abstract

As an increasing number of well measured type Ia supernovae (SNe Ia) become available, the statistical uncertainty on w has been reduced to the same size as the systematic uncertainty. The statistical error will decrease further in the near future, and hence the improvement of systematic uncertainties needs to be addressed, if further progress is to be made. We study how uncertainties in the primary reference spectrum - which are a main contribution to the systematic uncertainty budget - affect the measurement of the Dark Energy equation of state parameter w from SNe Ia. The increasing number of SN observations can be used to reduce the uncertainties by including perturbations of the reference spectrum as nuisance parameters in a cosmology fit, thus "self-calibrating" the Hubble diagram. We employ this method to real SNe data for the first time and find the perturbations of the reference spectrum consistent with zero at the 1%-level. For future surveys we estimate that ~3500 SNe will be required for our method to outperform the standard method of deriving the cosmological parameters.