Host Galaxy Identification for Supernova Surveys

Abstract

Host galaxy identification is a crucial step for modern supernova (SN) surveys such as the Dark Energy Survey and the Large Synoptic Survey Telescope, which will discover SNe by the thousands. Spectroscopic resources are limited, and so in the absence of real-time SN spectra these surveys must rely on host galaxy spectra to obtain accurate redshifts for the Hubble diagram and to improve photometric classification of SNe. In addition, SN luminosities are known to correlate with host-galaxy properties. Therefore, reliable identification of host galaxies is essential for cosmology and SN science. We simulate SN events and their locations within their host galaxies to develop and test methods for matching SNe to their hosts. We use both real and simulated galaxy catalog data from the Advanced Camera for Surveys General Catalog and MICECATv2.0, respectively. We also incorporate "hostless" SNe residing in undetected faint hosts into our analysis, with an assumed hostless rate of 5%. Our fully automated algorithm is run on catalog data and matches SNe to their hosts with 91% accuracy. We find that including a machine learning component, run after the initial matching algorithm, improves the accuracy (purity) of The Astronomical Journal, 152:154 (20pp), 2016 December doi:10.3847/0004-6256/152/6/154 © 2016. The American Astronomical Society. All rights reserved.1 the matching to 97% with a 2% cost in efficiency (true positive rate). Although the exact results are dependent on the details of the survey and the galaxy catalogs used, the method of identifying host galaxies we outline here can be applied to any transient survey.

OzDES multifibre spectroscopy for the Dark Energy Survey: 3-yr results and first data release

“…Note this value for redshift reliability is related to redshifts secured for the nominal SN host. The additional error of incorrectly identified hosts (shown in Gupta et al 2016, to be around 3%) is important for SN Ia cosmological analyses, but it beyond what we examine here.…”

First cosmology results using type Ia supernovae from the Dark Energy Survey: the effect of host galaxy properties on supernova luminosity

“…The host galaxies of the DES SNe Ia were identified using the 'Directional Light Radius' (DLR) methodology described in Sullivan et al (2006); Smith et al (2012); Gupta et al (2016); Sako et al (2018) and below in Appendix A. Following Gupta et al (2016) and Sako et al (2018), we only consider galaxies with d DLR < 7 to be candidates for the true host, and also require that the potential host be classified as a galaxy based on the CLASS_STAR SExtractor output (Soumagnac et al 2015). SNe with no galaxy matching this criteria are denoted hostless.…”

Measuring the Properties of Dark Energy with Photometrically Classified Pan-STARRS Supernovae. I. Systematic Uncertainty from Core-collapse Supernova Contamination

“…However, we also note that the 169 SN host pairs have preferentially brighter hosts than the full sample and have a median redshift of 0.21 compared to the median redshift of 0.3 for the full sample. It may be somewhat easier to mismatch a host galaxy at high-z as galaxies are more difficult to detect, but we expect this to be a subdominant effect as Gupta et al (2016) finds the fraction of mismatched hosts to be approximately constant at z<0.6 in a DES-like survey (which has similar depth to PS1 templates).…”

“…We use the redshift of the host galaxy with the lowest R if it has R5 following Sullivan et al (2006). See Gupta et al (2016) for a similar but more rigorous method of identifying SN host galaxies.…”