Host galaxy identification for supernova surveys

Ravi R. Gupta, Steve Kuhlmann, Eve Kovacs, Harold Spinka, Richard Kessler, Daniel A. Goldstein, Camille Liotine, Katarzyna Pomian, Chris B. D’Andrea, Mark Sullivan, Jorge Carretero, Francisco Javier Castander, Robert C. Nichol, David A. Finley, John A. Fischer, Ryan J. Foley, Alex G. Kim, Andreas Papadopoulos, Masao Sako, Daniel M. ScolnicMathew Smith, Brad E. Tucker, Syed Uddin, Rachel C. Wolf, Fang Yuan, Tim M. C. Abbott, Filipe B. Abdalla, Aurélien Benoit-lévy, Emmanuel Bertin, David Brooks, Aurelio Carnero Rosell, Matias Carrasco Kind, Carlos E. Cunha, Luiz A. N. da Costa, Shantanu Desai, Peter Doel, Tim F. Eifler, August E. Evrard, Brenna Flaugher, Pablo Fosalba, Enrique Gaztañaga, Daniel Gruen, Robert A. Gruendl, David J. James, Kyler Kuehn, Nikolay Kuropatkin, Marcio Antonio Geimba Maia, Jennifer L. Marshall, Ramon Miquel, Andrés A. Plazas, A. Kathy Romer, Eusebio Sánchez, Michael Schubnell, Ignacio Sevilla-Noarbe, Flávia Sobreira, Eric Suchyta, Molly E. C. Swanson, Gregory Tarle, Alistair R. Walker, William Wester

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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.
Original languageEnglish
Pages (from-to)154-173
Number of pages20
JournalThe Astronomical Journal
Volume152
Issue number6
DOIs
Publication statusPublished - 10 Nov 2016

Keywords

  • catalogs
  • galaxies: general
  • supernovae: general
  • surveys
  • RCUK
  • STFC
  • AST-1138766

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