Gupta, Ravi R. and Kuhlmann, Steve and Kovacs, Eve and Spinka, Harold and Kessler, Richard and Goldstein, Daniel A. and Liotine, Camille and Pomian, Katarzyna and D'Andrea, Chris B. and Sullivan, Mark and Carretero, Jorge and Castander, Francisco J. and Nichol, Robert C. and Finley, David A. and Fischer, John A. and Foley, Ryan J. and Kim, Alex G. and Papadopoulos, Andreas and Sako, Masao and Scolnic, Daniel M. and Smith, Mathew and Tucker, Brad E. and Uddin, Syed and Wolf, Rachel C. and Yuan, Fang and Abbott, Tim M. C. and Abdalla, Filipe B. and Benoit-Lévy, Aurélien and Bertin, Emmanuel and Brooks, David and Carnero Rosell, Aurelio and Carrasco Kind, Matias and Cunha, Carlos E. and da Costa, Luiz N. and Desai, Shantanu and Doel, Peter and Eifler, Tim F. and Evrard, August E. and Flaugher, Brenna and Fosalba, Pablo and Gaztañaga, Enrique and Gruen, Daniel and Gruendl, Robert and James, David J. and Kuehn, Kyler and Kuropatkin, Nikolay and Maia, Marcio A. G. and Marshall, Jennifer L. and Miquel, Ramon and Plazas, Andrés A. (2016) Host Galaxy Identification for Supernova Surveys. The Astronomical Journal, 152 (6): 154. ISSN 0004-6256
Full text not available from this repository.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 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.