Type Ia supernova Hubble diagrams with host galaxy photometric redshifts

TL;DR

This study evaluates how photometric host galaxy redshift uncertainties and contamination affect supernova Ia Hubble diagrams and cosmological parameter estimation, proposing methods to mitigate biases in photometric samples.

Contribution

It demonstrates the impact of redshift uncertainties and contamination on cosmological inferences from photometric SN Ia samples and tests methods to correct these biases.

Findings

Photometric redshift uncertainties bias cosmological parameters.

Sampling redshift resolution reduces bias slightly.

Full photometric redshift samples show significant bias without correction.

Abstract

The case of SN Ia Hubble diagrams from photometrically selected samples using photometric SN host galaxy redshifts is investigated. The host redshift uncertainties and the contamination by core collapse SNe are addressed. As a test, we use the 3-year photometric SN Ia sample of the SuperNova Legacy Survey (SNLS), made of 437 objects between 0.1 and 1.05 in redshift. We combine this sample with non-SNLS objects of the JLA spectroscopic sample, made of 501 objects mostly below 0.4 in redshift. We study two options for the origin of the redshifts of the photometric sample, either provided entirely from the host photometric redshift catalogue or a mixed origin where 75% of the sample can be assigned spectroscopic redshifts. Using light curve simulations subject to the same photometric selection as data, we study the impact of photometric redshift uncertainties and contamination on flat $\Lambda CDM$ fits to Hubble diagrams from such combined samples. We find that photometric redshifts and contamination lead to biased cosmological parameters. The magnitude of the bias is similar for both redshift options. This bias can be largely accounted for if photometric redshift uncertainties and contamination are taken into account when computing the SN magnitude bias correction due to selection effects. To reduce the cosmological bias further, we explore two methods to propagate redshift uncertainties into the cosmological likelihood computation, either by refitting photometric redshifts with cosmology or by sampling the redshift resolution function. Redshift refitting fails at correcting the cosmological bias whatever the redshift option, while sampling slightly reduces it in both cases. For actual data, we find compatible results with the JLA ones for mixed photometric and spectroscopic redshifts, while the full photometric option is biased but consistent with JLA when all uncertainties are included.